Research

Institute for Artificial Intelligence (KI)

General overview of the research scope of the Institute for Artificial Intelligence.

Area

Research Area	Department
Combining discrete probability distributions and Deep Learning	AC, MLS
Computer Vision	ISP
Constrained Learning	IS
Explainable AI	AC, MLS
Generative Modeling	AC, MLS
Geometric Deep Learning	AC, MLS
Graph Machine Learning	AC, MLS
Graph Neural Networks	AC, MLS, IS
Intelligent Human-Computer Interaction	AC
Intelligent Vehicles	AC, ISP
Knowledge Graphs	AC, MLS
Machine Learning and Simulation	AC, MLS, IS
Machine Learning for Natural Language Processing	AC, MLS
Machine Learning and Combinatorial Optimization	MLS
Multimodal Human Acitivty Analysis	ISP
Open-Set-, Open-Domain- and Uncertainty-aware Recognition	ISP
Optimization	IS
Physics-based Machine Learning	AC, MLS, IS
Reflection on Web, AI and Society	AC
Reinforcement Learning	IS
Resource- and Data-efficient Recognition	ISP
Robust Machine Learning	IS
Semantic Digital Twins	AC
Signal Processing	IS
Time Series Analysis	AC

Applications

Area	Department
Applications for Health Monitoring, Physiological Analysis and Medical Diagnostics	ISP
Applications of Foundation Models	AC
Assistive Systems and Robotics	AC, ISP
Knowledge Graphs and Semantic Digital Twins for Architecture, Engineering, and Construction	AC
Knowledge Graphs and Semantic Digital Twins for Engineering Design and Production	AC
Knowledge Graphs for Intelligent Vehicles	AC
Machine Learning for (Bio-) Medical Applications	MLS
Machine Learning for Molecules (Property Prediction, ML for PDEs)	MLS
ML for Physical Systems	AC, MLS
NLP for Biomedical Applications	AC

Institute Publications

2024
1. He, Y., Hernandez, D., Nayyeri, M., Xiong, B., Zhu, Y., Kharlamov, E., & Staab, S. (2024). Generating SROI- Ontologies via Knowledge Graph Query Embedding Learning. In ECAI 2024 (Vol. 392, pp. 4279–4286). IOS Press. https://doi.org/10.3233/FAIA241002
  - BibTeX
  BibTeX
  @preprint{he2024generating, abstract = {Query embedding approaches answer complex logical queries over incomplete knowledge graphs (KGs) by computing and operating on low-dimensional vector representations of entities, relations, and queries. However, current query embedding models heavily rely on excessively parameterized neural networks and cannot explain the knowledge learned from the graph. We propose a novel query embedding method, AConE, which explains the knowledge learned from the graph in the form of SROI− description logic axioms while being more parameter-efficient than most existing approaches. AConE associates queries to SROI− description logic concepts. Every SROI− concept is embedded as a cone in complex vector space, and each SROI− relation is embedded as a transformation that rotates and scales cones. We show theoretically that AConE can learn SROI− axioms, and defines an algebra whose operations correspond one-to-one to SROI− description logic concept constructs. Our empirical study on multiple query datasets shows that AConE achieves superior results over previous baselines with fewer parameters. Notably on the WN18RR dataset, AConE achieves significant improvement over baseline models. We provide comprehensive analyses showing that the capability to represent axioms positively impacts the results of query answering.}, author = {He, Yunjie and Hernandez, Daniel and Nayyeri, Mojtaba and Xiong, Bo and Zhu, Yuqicheng and Kharlamov, Evgeny and Staab, Steffen}, booktitle = {ECAI 2024}, doi = {10.3233/FAIA241002}, isbn = {9781643685489}, issn = {1879-8314}, month = {10}, pages = {4279 - 4286}, publisher = {IOS Press}, title = {Generating SROI- Ontologies via Knowledge Graph Query Embedding Learning}, type = {Publication}, url = {https://arxiv.org/abs/2407.09212}, volume = 392, year = 2024 }
2. Asma, Z., Hernández, D., Galárraga, L., Flouris, G., Fundulaki, I., & Hose, K. (2024, May). NPCS: Native Provenance Computation for SPARQL. Proceedings of the ACM Web Conference 2024 (WWW ’24), May13--17, 2024, Singapore, Singapore. WWW ’24, Singapore. https://doi.org/10.1145/3589334.3645557
  - BibTeX
  BibTeX
  @inproceedings{zubaria2024native, abstract = {The popularity of Knowledge Graphs (KGs) both in industry and academia owes credit to their flexible data model, suitable for data integration from multiple sources. Several KG-based applications such as trust assessment or view maintenance on dynamic data rely on the ability to compute provenance explanations for query results. The how-provenance of a query result is an expression that encodes the records (triples or facts) that explain its inclusion in the result set. This article proposes NPCS, a Native Provenance Computation approach for SPARQL queries. NPCS annotates query results with their how-provenance. By building upon spm-provenance semirings, NPCS supports both monotonic and non-monotonic SPARQL queries. Thanks to its reliance on query rewriting techniques, the approach is directly applicable to already deployed SPARQL engines using different reification schemes -- including RDF-star. Our experimental evaluation on two popular SPARQL engines (GraphDB and Stardog) shows that our novel query rewriting brings a significant runtime improvement over existing query rewriting solutions, scaling to RDF graphs with billions of triples.}, author = {Asma, Zubaria and Hernández, Daniel and Galárraga, Luis and Flouris, Giorgos and Fundulaki, Irini and Hose, Katja}, booktitle = {Proceedings of the ACM Web Conference 2024 (WWW '24), May13--17, 2024, Singapore, Singapore}, doi = {10.1145/3589334.3645557}, eventdate = {May 13 -17 2024}, eventtitle = {WWW '24}, isbn = {979-8-4007-0171-9/24/05}, language = {English}, month = {05}, publisher = {ACM}, title = {NPCS: Native Provenance Computation for SPARQL}, url = {https://doi.org/10.1145/3589334.3645557}, venue = {Singapore}, year = 2024 }
3. Blomqvist, E., García-Castro, R., Hernández, D., Hitzler, P., Lindecrantz, M., & Poveda-Villalón, M. (Eds.). (2024). Proceedings of the The 2nd International Workshop on Knowledge Graphs for Sustainability (KG4S 2024) colocated with the 21st Extended Semantic Web Conference (ESWC 2024) (Vol. 3753). Knowledge Graphs for Sustainability 2024, Hersonissos, Greece, May 27th, 2024. CEUR. https://ceur-ws.org/Vol-3753/
  - BibTeX
  BibTeX
  @proceedings{blomqvist2024proceedings, editor = {Blomqvist, Eva and García-Castro, Raúl and Hernández, Daniel and Hitzler, Pascal and Lindecrantz, Mikael and Poveda-Villalón, María}, eventdate = {2024-05-27}, eventtitle = {Knowledge Graphs for Sustainability 2024}, month = {05}, publisher = {CEUR}, title = {Proceedings of the The 2nd International Workshop on Knowledge Graphs for Sustainability (KG4S 2024) colocated with the 21st Extended Semantic Web Conference (ESWC 2024)}, url = {https://ceur-ws.org/Vol-3753/}, venue = {Hersonissos, Greece, May 27th, 2024}, volume = 3753, year = 2024 }
4. Elenter, J., Chamon, L. F. O., & Ribeiro, A. (2024, May). Near-Optimal Solutions of Constrained Learning Problems. Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7-11, 2024, Austria. https://doi.org/10.48550/arXiv.2403.11844
  - BibTeX
  BibTeX
  @inproceedings{elenter2024nearoptimal, abstract = {With the widespread adoption of machine learning systems, the need to curtail their behavior has become increasingly apparent. This is evidenced by recent advancements towards developing models that satisfy robustness, safety, and fairness requirements. These requirements can be imposed (with generalization guarantees) by formulating constrained learning problems that can then be tackled by dual ascent algorithms. Yet, though these algorithms converge in objective value, even in non-convex settings, they cannot guarantee that their outcome is feasible. Doing so requires randomizing over all iterates, which is impractical in virtually any modern applications. Still, final iterates have been observed to perform well in practice. In this work, we address this gap between theory and practice by characterizing the constraint violation of Lagrangian minimizers associated with optimal dual variables, despite lack of convexity. To do this, we leverage the fact that non-convex, finite-dimensional constrained learning problems can be seen as parametrizations of convex, functional problems. Our results show that rich parametrizations effectively mitigate the issue of feasibility in dual methods, shedding light on prior empirical successes of dual learning. We illustrate our findings in fair learning tasks.}, archiveprefix = {arXiv}, author = {Elenter, Juan and Chamon, Luiz F. O. and Ribeiro, Alejandro}, booktitle = {Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7-11, 2024, Austria}, doi = {10.48550/arXiv.2403.11844}, eprint = {2403.11844}, language = {en}, month = {05}, preprinturl = {https://arxiv.org/abs/2403.11844}, primaryclass = {cs.LG}, publisher = {ICLR}, title = {Near-Optimal Solutions of Constrained Learning Problems}, year = 2024 }
5. Elshani, D., Dervishaj, A., Hernández, D., Gudmundsson, K., Staab, S., & Wortmann, T. (2024). An Ontology for the Reuse and Tracking of Prefabricated Building Components. Proceedings of the The 2nd International Workshop on Knowledge Graphs for Sustainability (KG4S 2024) Colocated with the 21st Extended Semantic Web Conference (ESWC 2024), 3753, 53–64. https://ceur-ws.org/Vol-3753/paper5.pdf
  - BibTeX
  BibTeX
  @inproceedings{elshani2024ontology, author = {Elshani, Diellza and Dervishaj, Arlind and Hernández, Daniel and Gudmundsson, Kjartan and Staab, Steffen and Wortmann, Thomas}, booktitle = {Proceedings of the The 2nd International Workshop on Knowledge Graphs for Sustainability (KG4S 2024) colocated with the 21st Extended Semantic Web Conference (ESWC 2024)}, eventdate = {2024-05-27}, eventtitle = {Knowledge Graphs for Sustainability 2024}, month = {05}, pages = {53-64}, publisher = {CEUR}, title = {An Ontology for the Reuse and Tracking of Prefabricated Building Components}, url = {https://ceur-ws.org/Vol-3753/paper5.pdf}, venue = {Hersonissos, Greece, May 27th, 2024}, volume = 3753, year = 2024 }
6. Errica, F., & Niepert, M. (2024, May). Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks. Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7-11, 2024, Austria. https://doi.org/10.48550/arXiv.2305.10544
  - BibTeX
  BibTeX
  @inproceedings{errica2024tractable, abstract = {We introduce Graph-Induced Sum-Product Networks (GSPNs), a new probabilistic framework for graph representation learning that can tractably answer probabilistic queries. Inspired by the computational trees induced by vertices in the context of message-passing neural networks, we build hierarchies of sum-product networks (SPNs) where the parameters of a parent SPN are learnable transformations of the a-posterior mixing probabilities of its children's sum units. Due to weight sharing and the tree-shaped computation graphs of GSPNs, we obtain the efficiency and efficacy of deep graph networks with the additional advantages of a probabilistic model. We show the model's competitiveness on scarce supervision scenarios, under missing data, and for graph classification in comparison to popular neural models. We complement the experiments with qualitative analyses on hyper-parameters and the model's ability to answer probabilistic queries.}, archiveprefix = {arXiv}, author = {Errica, Federico and Niepert, Mathias}, booktitle = {Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7-11, 2024, Austria}, doi = {10.48550/arXiv.2305.10544}, eprint = {2305.10544}, language = {en}, month = {05}, preprinturl = {https://arxiv.org/abs/2305.10544v2}, primaryclass = {cs.LG}, title = {Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks}, year = 2024 }
7. Hagnberger, J., Kalimuthu, M., Musekamp, D., & Niepert, M. (2024, May). Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent PDEs. Proceedings of the AI4DifferentialEquations in Science Workshop at ICLR 2024, May 7-11, 2024, Austria.
  - BibTeX
  BibTeX
  @inproceedings{hagnberger2024vectorized, abstract = {Neural Operators are a recent class of data-driven models for learning solutions to Partial Differential Equations (PDEs). Traditionally, these models are trained in an autoregressive fashion using data collected at discrete time points in the evolution of the PDE. This setup gives rise to two problems: (i) poor temporal generalization due to error accumulation and (ii) poor zero-shot super-resolution capabilities. To address these issues, we propose Vectorized Conditional Neural Fields (VCNeF), a general framework that utilizes transformers and implicit neural representations to efficiently solve time-dependent PDEs of varying coefficients. A comprehensive evaluation of VCNeF on the challenging 1D and 2D PDEs from PDEBench demonstrates the superiority of our model over four state-of-the-art baselines. Furthermore, our proposed model achieves faster inference and generalizes better to unseen PDE parameters than the compared models.}, author = {Hagnberger, Jan and Kalimuthu, Marimuthu and Musekamp, Daniel and Niepert, Mathias}, booktitle = {Proceedings of the AI4DifferentialEquations in Science workshop at ICLR 2024, May 7-11, 2024, Austria}, month = {05}, publisher = {ICLR}, title = {Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent PDEs}, year = 2024 }
8. Liu, A., Niepert, M., & den Broeck, G. V. (2024, May). Image Inpainting via Tractable Steering of Diffusion Models. Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7--11, 2024, Austria. https://doi.org/10.48550/arXiv.2401.03349
  - BibTeX
  BibTeX
  @inproceedings{liu2023image, abstract = {Diffusion models are the current state of the art for generating photorealistic images. However, controlling the sampling process for constrained image generation tasks such as inpainting remains challenging since exact conditioning on such constraints is intractable. While existing methods use various techniques to approximate the constrained posterior, this paper proposes to exploit the ability of Tractable Probabilistic Models (TPMs) to exactly and efficiently compute the constrained posterior, and to leverage this signal to steer the denoising process of diffusion models. Specifically, this paper adopts a class of expressive TPMs termed Probabilistic Circuits (PCs). Building upon prior advances, we further scale up PCs and make them capable of guiding the image generation process of diffusion models. Empirical results suggest that our approach can consistently improve the overall quality and semantic coherence of in painted images across three natural image datasets (i.e., CelebA-HQ, ImageNet, and LSUN) with only ~10% additional computational overhead brought by the TPM.}, archiveprefix = {arXiv}, author = {Liu, Anji and Niepert, Mathias and den Broeck, Guy Van}, booktitle = {Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7--11, 2024, Austria}, doi = {10.48550/arXiv.2401.03349}, eprint = {2401.03349}, language = {en}, month = {05}, preprinturl = {https://arxiv.org/abs/2401.03349v1}, primaryclass = {cs.CV}, publisher = {ICLR}, title = {Image Inpainting via Tractable Steering of Diffusion Models}, venue = {Austria}, year = 2024 }
9. Qian, C., Manolache, A., Ahmed, K., Zeng, Z., den Broeck, G. V., Niepert, M., & Morris, C. (2024, May). Probabilistically Rewired Message-Passing Neural Networks. Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7--11, 2024, Austria. https://doi.org/10.48550/arXiv.2310.02156
  - BibTeX
  BibTeX
  @inproceedings{qian2023probabilistically, abstract = {Message-passing graph neural networks (MPNNs) emerged as powerful tools for processing graph-structured input. However, they operate on a fixed input graph structure, ignoring potential noise and missing information. Furthermore, their local aggregation mechanism can lead to problems such as over-squashing and limited expressive power in capturing relevant graph structures. Existing solutions to these challenges have primarily relied on heuristic methods, often disregarding the underlying data distribution. Hence, devising principled approaches for learning to infer graph structures relevant to the given prediction task remains an open challenge. In this work, leveraging recent progress in exact and differentiable k-subset sampling, we devise probabilistically rewired MPNNs (PR-MPNNs), which learn to add relevant edges while omitting less beneficial ones. For the first time, our theoretical analysis explores how PR-MPNNs enhance expressive power, and we identify precise conditions under which they outperform purely randomized approaches. Empirically, we demonstrate that our approach effectively mitigates issues like over-squashing and under-reaching. In addition, on established real-world datasets, our method exhibits competitive or superior predictive performance compared to traditional MPNN models and recent graph transformer architectures.}, archiveprefix = {arXiv}, author = {Qian, Chendi and Manolache, Andrei and Ahmed, Kareem and Zeng, Zhe and den Broeck, Guy Van and Niepert, Mathias and Morris, Christopher}, booktitle = {Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7--11, 2024, Austria}, doi = {10.48550/arXiv.2310.02156}, eprint = {2310.02156}, language = {en}, month = {05}, preprinturl = {https://arxiv.org/abs/2310.02156}, primaryclass = {cs.LG}, publisher = {ICLR}, title = {Probabilistically Rewired Message-Passing Neural Networks}, year = 2024 }
10. Seifer, P., Hernández, D., Lämmel, R., & Staab, S. (2024, May). From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries. Proceedings of the ACM Web Conference 2024 (WWW ’24), May13--17, 2024, Singapore, Singapore. WWW ’24, Singapore. https://doi.org/10.1145/3589334.3645550
  - BibTeX
  BibTeX
  @inproceedings{seifer2024shapes, abstract = {SPARQL CONSTRUCT queries allow for the specification of data processing pipelines that transform given input graphs into new output graphs. It is now common to constrain graphs through SHACL shapes allowing users to understand which data they can expect and which not. However, it becomes challenging to understand what graph data can be expected at the end of a data processing pipeline without knowing the particular input data: Shape constraints on the input graph may affect the output graph, but may no longer apply literally, and new shapes may be imposed by the query template. In this paper, we study the derivation of shape constraints that hold on all possible output graphs of a given SPARQL CONSTRUCT query. We assume that the SPARQL CONSTRUCT query is fixed, e.g., being part of a program, whereas the input graphs adhere to input shape constraints but may otherwise vary over time and, thus, are mostly unknown. We study a fragment of SPARQL CONSTRUCT queries (SCCQ) and a fragment of SHACL (Simple SHACL). We formally define the problem of deriving the most restrictive set of Simple SHACL shapes that constrain the results from evaluating a SCCQ over any input graph restricted by a given set of Simple SHACL shapes. We propose and implement an algorithm that statically analyses input SHACL shapes and CONSTRUCT queries and prove its soundness and complexity.}, author = {Seifer, Philipp and Hernández, Daniel and Lämmel, Ralf and Staab, Steffen}, booktitle = {Proceedings of the ACM Web Conference 2024 (WWW '24), May13--17, 2024, Singapore, Singapore}, doi = {10.1145/3589334.3645550}, eventdate = {May 13–17, 2024}, eventtitle = {WWW '24}, isbn = {979-8-4007-0171-9/24/05}, month = {05}, preprinturl = {https://arxiv.org/abs/2402.08509}, publisher = {ACM}, title = {From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries}, url = {https://doi.org/10.1145/3589334.3645550}, venue = {Singapore}, year = 2024 }
11. Tran, H.-C., Nguyen, D. M. H., Nguyen, M.-D., Le, N. H., & T. Nguyen, B. (2024, May). Energy Minimizing-based Token Merging for Accelerating Transformers. Proceedings of Practical ML for Low Resource Settings in Science Workshop at ICLR 2024, May 7-11, 2024, Austria.
  - BibTeX
  BibTeX
  @inproceedings{tran2024energy, abstract = {Model compression has been an active research field that has been used to reduce the size and complexity of the model. In a recent noteworthy study, ToMe and its variants utilize the Bipartite Soft Matching (BSM) algorithm in which tokens representing patches in an image are split into two sets, and top-k similar tokens from one set are merged. This approach utilizes pre-trained weights, enhances speed, and reduces memory usage. However, these algorithms have some drawbacks. First, the choice of a token-splitting strategy significantly influences algorithm performance since tokens in one set can only perceive tokens in the other set, leading to mis-merging issues. Furthermore, although ToMe is effective in the initial layers, it becomes increasingly problematic in deeper layers as the number of tokens diminishes because of damaged informative tokens. To address these limitations, rather than relying on specific splitting strategies like BSM, we propose a new algorithm called PiToMe. Specifically, we prioritize the protection of informative tokens using an additional factor called energy score. In experiments, PiToMe achieved up to a 50% memory reduction while exhibiting superior off-the-shelf performance on image classification ( keeping a 1.71% average performance drop compared to 2.6% for ToMe) and image-text retrieval (1.35% average performance drop compared to 6.89% for ToMe) compared to previous BSM-based approaches dependent solely on token similarity.}, author = {Tran, Hoai-Chau and Nguyen, Duy Minh Ho and Nguyen, Manh-Duy and Le, Ngan Hoang and T. Nguyen, Binh}, booktitle = {Proceedings of Practical ML for Low Resource Settings in Science workshop at ICLR 2024, May 7-11, 2024, Austria}, language = {en}, month = {05}, publisher = {ICLR}, title = {Energy Minimizing-based Token Merging for Accelerating Transformers}, year = 2024 }
12. Zubaria, A., Hernández, D., Galárraga, L., Flouris, G., Fundulaki, I., & Hose, K. (2024, May). NPCS: Native Provenance Computation for SPARQL. Proceedings of the ACM Web Conference 2024 (WWW ’24), May13--17, 2024, Singapore, Singapore. WWW ’24, Singapore. https://doi.org/10.1145/3589334.3645557
  - BibTeX
  BibTeX
  @conference{zubaria2024native, abstract = {The popularity of Knowledge Graphs (KGs) both in industry and academia owes credit to their flexible data model, suitable for data integration from multiple sources. Several KG-based applications such as trust assessment or view maintenance on dynamic data rely on the ability to compute provenance explanations for query results. The how-provenance of a query result is an expression that encodes the records (triples or facts) that explain its inclusion in the result set. This article proposes NPCS, a Native Provenance Computation approach for SPARQL queries. NPCS annotates query results with their how-provenance. By building upon spm-provenance semirings, NPCS supports both monotonic and non-monotonic SPARQL queries. Thanks to its reliance on query rewriting techniques, the approach is directly applicable to already deployed SPARQL engines using different reification schemes -- including RDF*. Our experimental evaluation on two popular SPARQL engines (GraphDB and Stardog) shows that our novel query rewriting brings a significant runtime improvement over existing query rewriting solutions, scaling to RDF graphs with billions of triples.}, author = {Zubaria, Asma and Hernández, Daniel and Galárraga, Luis and Flouris, Giorgos and Fundulaki, Irini and Hose, Katja}, booktitle = {Proceedings of the ACM Web Conference 2024 (WWW '24), May13--17, 2024, Singapore, Singapore}, doi = {10.1145/3589334.3645557}, eventdate = {May 13 -17 2024}, eventtitle = {WWW '24}, language = {English}, month = {05}, publisher = {ACM}, title = {NPCS: Native Provenance Computation for SPARQL}, url = {https://doi.org/10.1145/3589334.3645557}, venue = {Singapore}, year = 2024 }
13. "Hosseini, A. S., & "Staab, S. (2024). Disambiguating Emotional Connotations of Words Using Contextualized Word Representations.
  - BibTeX
  BibTeX
  @inproceedings{hosseini2024disambiguating, author = {"Hosseini, Akram Sadat" and "Staab, Steffen"}, title = {Disambiguating Emotional Connotations of Words Using Contextualized Word Representations}, year = 2024 }
14. Asma, Z., Hernandez, D., Galárraga, L., Flouris, G., Fundulaki, I., & Hose, K. (2024). Code and benchmark for NPCS, a Native Provenance Computation for SPARQL. https://doi.org/10.18419/darus-3973
  - BibTeX
  BibTeX
  @misc{asma2024benchmark, abstract = {Code for the implementation and benchmark of NPCS, a Native Provenance Computation for SPARQL.The code in this dataset includes the implementation of the NPCS system, which is a middleware for SPARQL endpoints that rewrites queries to queries that annotate answers with provenance polynomials (i.e., how-provenance data). The translation rules implemented for the query rewriting can be seen in the paper.Also, the code contains scripts that include scripts and services to automatize the query execution.We use GraphDB (version 10.2.0) and Stardog (version 9.1.0) for the SPARQL endpoints. Because of the license restrictions, these software products cannot be included in this dataset and must be downloaded from the respective vendors. Also, the data must be loaded using the respective bulk loaders of GraphDB and Stardog.The datasets used in the experiments can be generated synthetic dataset generator of the WatDiv benchmark. The Wikidata dataset corresponds to the full RDF dump from May 22, 2023.Do not hesitate to contact the authors for any inquiries. }, affiliation = {Asma, Zubaria/FORT-ICS, Hernández, Daniel/University of Stuttgart, Galárraga, Luis/INRIA, Flouris, Giorgos/FORTH-ICS, Fundulaki, Irini/FORTH-ICS, Hose, Katja/TU Wien}, author = {Asma, Zubaria and Hernandez, Daniel and Galárraga, Luis and Flouris, Giorgos and Fundulaki, Irini and Hose, Katja}, doi = {10.18419/darus-3973}, howpublished = {Software}, note = {Related to: A. Zubaria, D. Hernández, L. Galárraga, G. Flouris, I. Fundulaki, and K. Hose. NPCS: Native Provenance Computation for SPARQL. Proceedings of the ACM Web Conference 2024 (WWW '24), May13-17, 2024, Singapore, Singapore, ACM, (May 2024). doi: 10.1145/3589334.3645557}, orcid-numbers = {Asma, Zubaria/0000-0002-9402-7487, Hernández, Daniel/0000-0002-7896-0875, Galárraga, Luis/0000-0002-0241-5379, Flouris, Giorgos/0000-0002-8937-4118, Fundulaki, Irini/0000-0002-4812-9896, Hose, Katja/0000-0001-7025-8099}, title = {Code and benchmark for NPCS, a Native Provenance Computation for SPARQL}, url = {https://doi.org/10.18419/darus-3973}, year = 2024 }
15. Crum, E., Santis, A. D., Ovide, M., Pan, J., Pisu, A., Lazzari, N., & Rudolph, S. (2024). Enriching Ontologies with Disjointness Axioms using Large Language Models. International Semantic Web Conference 2024. https://doi.org/10.48550/arXiv.2410.03235
  - BibTeX
  BibTeX
  @inproceedings{crum2024enrichingontologiesdisjointnessaxioms, archiveprefix = {arXiv}, author = {Crum, Elias and Santis, Antonio De and Ovide, Manon and Pan, Jiaxin and Pisu, Alessia and Lazzari, Nicolas and Rudolph, Sebastian}, booktitle = {Workshop on Knowledge Base Construction from Pre-Trained Language Models}, doi = {https://doi.org/10.48550/arXiv.2410.03235}, eprint = {2410.03235}, journal = {International Semantic Web Conference 2024}, language = {en}, primaryclass = {cs.AI}, title = {Enriching Ontologies with Disjointness Axioms using Large Language Models}, url = {https://arxiv.org/abs/2410.03235}, year = 2024 }
16. Das, A., Fathallah, N., & Obretincheva, N. (2024). Navigating Nulls, Numbers and Numerous Entities: Robust Knowledge Base Construction from Large Language Models. In Knowledge Base Construction from Pre-trained Language Models Challenge Workshop, ISWC’24.
  - BibTeX
  BibTeX
  @preprint{das2024navigating, author = {Das, Arunav and Fathallah, Nadeen and Obretincheva, Nicole}, booktitle = {Knowledge Base Construction from Pre-trained Language Models Challenge Workshop, ISWC'24.}, title = {Navigating Nulls, Numbers and Numerous Entities: Robust Knowledge Base Construction from Large Language Models.}, year = 2024 }
17. Ding, Z., Cai, H., Wu, J., Ma, Y., Liao, R., Xiong, B., & Tresp, V. (2024). zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models. Annual Conference of the North American Chapter of the Association for Computational Linguistics. https://arxiv.org/abs/2311.10112
  - BibTeX
  BibTeX
  @article{ding2023zeroshot, abbr = {NAACL}, arxiv = {2311.10112}, author = {Ding, Zifeng and Cai, Heling and Wu, Jingpei and Ma, Yunpu and Liao, Ruotong and Xiong, Bo and Tresp, Volker}, journal = {Annual Conference of the North American Chapter of the Association for Computational Linguistics}, title = {zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models}, url = {https://arxiv.org/abs/2311.10112}, year = 2024 }
18. Fathallah, N., Bhole, M., & Staab, S. (2024). Empowering the Deaf and Hard of Hearing Community: Improving Video Captions with Large Language Models. In In Proceedings of the 11th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion.
  - BibTeX
  BibTeX
  @preprint{fathallah2024empowering, author = {Fathallah, Nadeen and Bhole, Monika and Staab, Steffen}, booktitle = {In Proceedings of the 11th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion. }, title = {Empowering the Deaf and Hard of Hearing Community: Improving Video Captions with Large Language Models}, year = 2024 }
19. Fathallah, N., Das, A., De Giorgis, G., Poltronieri, A., Haase, P., & Kovriguina, L. (2024). NeOn-GPT: A Large Language Model-Powered Pipeline for Ontology Learning. Special Track on Large Language Models for Knowledge Engineering, Extended Semantic Web Conference, 2024. (ESWC 2024). https://doi.org/10.5281/ZENODO.11221930
  - BibTeX
  BibTeX
  @inproceedings{https://doi.org/10.5281/zenodo.11221930, author = {Fathallah, Nadeen and Das, Arunav and De Giorgis, Giorgis and Poltronieri, Andrea and Haase, Peter and Kovriguina, Liubov}, booktitle = {Special Track on Large Language Models for Knowledge Engineering, Extended Semantic Web Conference, 2024. (ESWC 2024)}, copyright = {Creative Commons Attribution 4.0 International}, doi = {10.5281/ZENODO.11221930}, language = {en}, title = {NeOn-GPT: A Large Language Model-Powered Pipeline for Ontology Learning.}, url = {https://zenodo.org/doi/10.5281/zenodo.11221930}, year = 2024 }
20. Fathallah, N., Staab, S., & Algergawy, A. (2024). LLMs4Life: Large language models for ontology learning in life sciences. In In Proceedings of the ELMKE Workshop on Evaluation of Language Models in Knowledge Engineering co-located with EKAW-24 (24th International Conference on Knowledge Engineering and Knowledge Management).
  - BibTeX
  BibTeX
  @preprint{fathallah2024llms4life, author = {Fathallah, Nadeen and Staab, Steffen and Algergawy, Alsayed}, booktitle = {In Proceedings of the ELMKE Workshop on Evaluation of Language Models in Knowledge Engineering co-located with EKAW-24 (24th International Conference on Knowledge Engineering and Knowledge Management).}, title = {LLMs4Life: Large language models for ontology learning in life sciences.}, year = 2024 }
21. Hagnberger, J., Kalimuthu, M., Musekamp, D., & Niepert, M. (2024). Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent Parametric Partial Differential Equations. In Proceedings of the 41st International Conference on Machine Learning (ICML 2024). https://arxiv.org/abs/2406.03919
  - BibTeX
  BibTeX
  @inproceedings{hagnberger2024vectorizedconditionalneuralfields, abstract = {Transformer models are increasingly used for solving Partial Differential Equations (PDEs). Several adaptations have been proposed, all of which suffer from the typical problems of Transformers, such as quadratic memory and time complexity. Furthermore, all prevalent architectures for PDE solving lack at least one of several desirable properties of an ideal surrogate model, such as (i) generalization to PDE parameters not seen during training, (ii) spatial and temporal zero-shot super-resolution, (iii) continuous temporal extrapolation, (iv) support for 1D, 2D, and 3D PDEs, and (v) efficient inference for longer temporal rollouts. To address these limitations, we propose Vectorized Conditional Neural Fields (VCNeFs), which represent the solution of time-dependent PDEs as neural fields. Contrary to prior methods, however, VCNeFs compute, for a set of multiple spatio-temporal query points, their solutions in parallel and model their dependencies through attention mechanisms. Moreover, VCNeF can condition the neural field on both the initial conditions and the parameters of the PDEs. An extensive set of experiments demonstrates that VCNeFs are competitive with and often outperform existing ML-based surrogate models.}, archiveprefix = {arXiv}, author = {Hagnberger, Jan and Kalimuthu, Marimuthu and Musekamp, Daniel and Niepert, Mathias}, booktitle = {In Proceedings of the 41st International Conference on Machine Learning (ICML 2024)}, eprint = {2406.03919}, language = {en}, primaryclass = {cs.LG}, title = {Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent Parametric Partial Differential Equations}, url = {https://arxiv.org/abs/2406.03919}, year = 2024 }
22. Hedeshy, R., Menges, R., & Staab, S. (2024). Raw audio samples of the CNVVE dataset. DaRUS. https://doi.org/10.18419/DARUS-3897
  - BibTeX
  BibTeX
  @dataset{https://doi.org/10.18419/darus-3897, abstract = {This CNVVE Dataset contains raw audio samples encompassing six distinct classes of voice expressions, namely “Uh-huh” or “mm-hmm”, “Uh-uh” or“mm-mm”, “Hush” or “Shh”, “Psst”, “Ahem”, and Continuous humming, e.g., “hmmm.” Audio samples of each class are found in the respective folders. The samples are recorded through a dedicated website for data collection that defines the purpose and type of voice data by providing example recordings to participants as well as the expressions’ written equivalent, e.g., “Uh-huh”. Audio recordings were automatically saved in the .wav format and kept anonymous, with a sampling rate of 48 kHz and a bit depth of 32 bits.This dataset contains a raw version of the samples. A cleaned version of these samples can be found on https://doi.org/10.18419/darus-3898. For more info, please check the paper or feel free to contact the authors for any inquiries.}, author = {Hedeshy, Ramin and Menges, Raphael and Staab, Steffen}, doi = {10.18419/DARUS-3897}, publisher = {DaRUS}, title = {Raw audio samples of the CNVVE dataset}, url = {https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/darus-3897}, year = 2024 }
23. Hedeshy, R., Menges, R., & Staab, S. (2024). CNVVE Dataset clean audio samples. DaRUS. https://doi.org/10.18419/DARUS-3898
  - BibTeX
  BibTeX
  @dataset{https://doi.org/10.18419/darus-3898, abstract = {This CNVVE Dataset contains clean audio samples encompassing six distinct classes of voice expressions, namely “Uh-huh” or “mm-hmm”, “Uh-uh” or“mm-mm”, “Hush” or “Shh”, “Psst”, “Ahem”, and Continuous humming, e.g., “hmmm.” Audio samples of each class are found in the respective folders. These audio samples have undergone a thorough cleaning process. The raw samples are published in https://doi.org/10.18419/darus-3897. Initially, we applied the Google WebRTC voice activity detection (VAD) algorithm on the given audio files to remove noise or silence from the collected voice signals. The intensity was set to "2", which could be a value between "1" and "3". However, because of variations in the data, some files required additional manual cleaning. These outliers, characterized by sharp click sounds (such as those occurring at the end of recordings), were addressed. The samples are recorded through a dedicated website for data collection that defines the purpose and type of voice data by providing example recordings toparticipants as well as the expressions’ written equivalent, e.g., “Uh-huh”. Audio recordings were automatically saved in the .wav format and keptanonymous, with a sampling rate of 48 kHz and a bit depth of 32 bits. For more info, please check the paper or feel free to contact the authors for any inquiries.}, author = {Hedeshy, Ramin and Menges, Raphael and Staab, Steffen}, doi = {10.18419/DARUS-3898}, publisher = {DaRUS}, title = {CNVVE Dataset clean audio samples}, url = {https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/darus-3898}, year = 2024 }
24. Jalali Farahani, F., Hanke, S., Dima, C., Heiberger, R. H., & Staab, S. (2024). Who is targeted? Detecting social group mentions in online political discussions. Companion Publication of the 16th ACM Web Science Conference, 24–25. https://doi.org/10.1145/3630744.3658412
  - BibTeX
  BibTeX
  @inproceedings{10.1145/3630744.3658412, abstract = {Social groups are central to political discussions. However, detecting social groups in text often relies on pre-determined socio-demographic categories or supervised learning methods that require extensive hand-labeled datasets. In this paper, we propose a methodology designed to leverage the potential of Large Language Models (LLMs) for the identification and annotation of social groups in text. The experiments show that open LLMs like Llama-2-70B-Chat and Mixtral-8-7B can reliably be used to annotate social groups in a few-shot scenario without the need for supervised learning. The automatically obtained annotations largely match human annotations on random samples from the Reddit Politosphere, resulting in micro-F1 scores of 0.71 and 0.83, respectively.}, address = {New York, NY, USA}, author = {Jalali Farahani, Farane and Hanke, Sara and Dima, Corina and Heiberger, Raphael Heiko and Staab, Steffen}, booktitle = {Companion Publication of the 16th ACM Web Science Conference}, doi = {10.1145/3630744.3658412}, isbn = {9798400704536}, location = {Stuttgart, Germany}, numpages = {2}, pages = {24–25}, publisher = {Association for Computing Machinery}, series = {Websci Companion '24}, title = {Who is targeted? Detecting social group mentions in online political discussions}, url = {https://doi.org/10.1145/3630744.3658412}, year = 2024 }
25. Nguyen, D. M. H., Le, A. T., Nguyen, T. Q., Diep, N. T., Nguyen, T., Duong-Tran, D., Peters, J., Shen, L., Niepert, M., & Sonntag, D. (2024). Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model. Proceedings of Machine Learning Research. https://arxiv.org/abs/2407.04489
  - BibTeX
  BibTeX
  @inproceedings{nguyen2024dudedualdistributionawarecontext, abstract = {Prompt learning methods are gaining increasing attention due to their ability to customize large vision-language models to new domains using pre-trained contextual knowledge and minimal training data. However, existing works typically rely on optimizing unified prompt inputs, often struggling with fine-grained classification tasks due to insufficient discriminative attributes. To tackle this, we consider a new framework based on a dual context of both domain-shared and class-specific contexts, where the latter is generated by Large Language Models (LLMs) such as GPTs. Such dual prompt methods enhance the model's feature representation by joining implicit and explicit factors encoded in LLM knowledge. Moreover, we formulate the Unbalanced Optimal Transport (UOT) theory to quantify the relationships between constructed prompts and visual tokens. Through partial matching, UOT can properly align discrete sets of visual tokens and prompt embeddings under different mass distributions, which is particularly valuable for handling irrelevant or noisy elements, ensuring that the preservation of mass does not restrict transport solutions. Furthermore, UOT's characteristics integrate seamlessly with image augmentation, expanding the training sample pool while maintaining a reasonable distance between perturbed images and prompt inputs. Extensive experiments across few-shot classification and adapter settings substantiate the superiority of our model over current state-of-the-art baselines.}, archiveprefix = {arXiv}, author = {Nguyen, Duy M. H. and Le, An T. and Nguyen, Trung Q. and Diep, Nghiem T. and Nguyen, Tai and Duong-Tran, Duy and Peters, Jan and Shen, Li and Niepert, Mathias and Sonntag, Daniel}, booktitle = {Proceedings of Machine Learning Research}, eprint = {2407.04489}, language = {en}, primaryclass = {cs.CV}, title = {Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model}, url = {https://arxiv.org/abs/2407.04489}, venue = {Asian Conference on Machine Learning, ACML 2024}, year = 2024 }
26. Nguyen, D. M. H., Lukashina, N., Nguyen, T., Le, A. T., Nguyen, T., Ho, N., Peters, J., Sonntag, D., Zaverkin, V., & Niepert, M. (2024). Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks. In Proceedings of the 41st International Conference on Machine Learning (ICML 2024). https://arxiv.org/abs/2402.01975
  - BibTeX
  BibTeX
  @inproceedings{nguyen2024structureawaree3invariantmolecularconformer, abstract = {A molecule's 2D representation consists of its atoms, their attributes, and the molecule's covalent bonds. A 3D (geometric) representation of a molecule is called a conformer and consists of its atom types and Cartesian coordinates. Every conformer has a potential energy, and the lower this energy, the more likely it occurs in nature. Most existing machine learning methods for molecular property prediction consider either 2D molecular graphs or 3D conformer structure representations in isolation. Inspired by recent work on using ensembles of conformers in conjunction with 2D graph representations, we propose E(3)-invariant molecular conformer aggregation networks. The method integrates a molecule's 2D representation with that of multiple of its conformers. Contrary to prior work, we propose a novel 2D-3D aggregation mechanism based on a differentiable solver for the Fused Gromov-Wasserstein Barycenter problem and the use of an efficient conformer generation method based on distance geometry. We show that the proposed aggregation mechanism is E(3) invariant and propose an efficient GPU implementation. Moreover, we demonstrate that the aggregation mechanism helps to significantly outperform state-of-the-art molecule property prediction methods on established datasets.}, archiveprefix = {arXiv}, author = {Nguyen, Duy M. H. and Lukashina, Nina and Nguyen, Tai and Le, An T. and Nguyen, TrungTin and Ho, Nhat and Peters, Jan and Sonntag, Daniel and Zaverkin, Viktor and Niepert, Mathias}, booktitle = {In Proceedings of the 41st International Conference on Machine Learning (ICML 2024)}, eprint = {2402.01975}, language = {en}, primaryclass = {cs.LG}, title = {Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks}, url = {https://arxiv.org/abs/2402.01975}, year = 2024 }
27. Pan, J., Nayyeri, M., Li, Y., & Staab, S. (2024). HGE: Embedding Temporal Knowledge Graphs in a Product Space of Heterogeneous Geometric Subspaces. Thirty-Eighth Conference on Artificial Intelligence, AAAI, 2024, Vancouver, Canada, February 22 – February 25, 2024,.
  - BibTeX
  BibTeX
  @inproceedings{pan2023hge, abstract = {Temporal knowledge graphs represent temporal facts (s,p,o,τ) relating a subject s and an object o via a relation label p at time τ, where τ could be a time point or time interval. Temporal knowledge graphs may exhibit static temporal patterns at distinct points in time and dynamic temporal patterns between different timestamps. In order to learn a rich set of static and dynamic temporal patterns and apply them for inference, several embedding approaches have been suggested in the literature. However, as most of them resort to single underlying embedding spaces, their capability to model all kinds of temporal patterns was severely limited by having to adhere to the geometric property of their one embedding space. We lift this limitation by an embedding approach that maps temporal facts into a product space of several heterogeneous geometric subspaces with distinct geometric properties, i.e.\ Complex, Dual, and Split-complex spaces. In addition, we propose a temporal-geometric attention mechanism to integrate information from different geometric subspaces conveniently according to the captured relational and temporal information. Experimental results on standard temporal benchmark datasets favorably evaluate our approach against state-of-the-art models.}, author = {Pan, Jiaxin and Nayyeri, Mojtaba and Li, Yinan and Staab, Steffen}, booktitle = {Thirty-eighth Conference on Artificial Intelligence, {AAAI}, 2024, Vancouver, Canada, February 22 – February 25, 2024,}, eventdate = {22 February 2024 – 25 February 2024}, preprinturl = {https://arxiv.org/abs/2312.13680}, title = {HGE: Embedding Temporal Knowledge Graphs in a Product Space of Heterogeneous Geometric Subspaces}, year = 2024 }
28. Pan, X., Hernández, D., Seifer, P., Lämmel, R., & Staab, S. (2024). eSPARQL: Representing and Reconciling Agnostic and Atheistic Beliefs in RDF-star Knowledge Graphs. https://arxiv.org/abs/2407.21483
  - BibTeX
  BibTeX
  @preprint{pan2024esparqlrepresentingreconcilingagnostic, archiveprefix = {arXiv}, author = {Pan, Xiny and Hernández, Daniel and Seifer, Philipp and Lämmel, Ralf and Staab, Steffen}, eprint = {2407.21483}, primaryclass = {cs.AI}, title = {eSPARQL: Representing and Reconciling Agnostic and Atheistic Beliefs in RDF-star Knowledge Graphs}, url = {https://arxiv.org/abs/2407.21483}, year = 2024 }
29. Peng, K., Yin, C., Zheng, J., Liu, R., Schneider, D., Zhang, J., Yang, K., Sarfraz, M. S., Stiefelhagen, R., & Roitberg, A. (2024). Navigating Open Set Scenarios for Skeleton-based Action Recognition. The 38th Annual AAAI Conference on Artificial Intelligence. https://arxiv.org/abs/2312.06330
  - BibTeX
  BibTeX
  @article{peng2023navigating, author = {Peng, Kunyu and Yin, Cheng and Zheng, Junwei and Liu, Ruiping and Schneider, David and Zhang, Jiaming and Yang, Kailun and Sarfraz, M Saquib and Stiefelhagen, Rainer and Roitberg, Alina}, booktitle = {The 38th Annual AAAI Conference on Artificial Intelligence}, title = {Navigating Open Set Scenarios for Skeleton-based Action Recognition}, url = {https://arxiv.org/abs/2312.06330}, year = 2024 }
30. Potyka, N., Zhu, Y., He, Y., Kharlamov, E., & Staab, S. (2024). Robust Knowledge Extraction from Large Language Models using Social Choice Theory. In Proceedings of the 23rd International Conference on Autonomous Agents and Multi-Agent Systems. https://arxiv.org/abs/2312.14877
  - BibTeX
  BibTeX
  @inproceedings{noauthororeditor2023robust, abstract = {Large-language models (LLMs) have the potential to support a wide range of applications like conversational agents, creative writing, text improvement, and general query answering. However, they are ill-suited for query answering in high-stake domains like medicine because they generate answers at random and their answers are typically not robust - even the same query can result in different answers when prompted multiple times. In order to improve the robustness of LLM queries, we propose using ranking queries repeatedly and to aggregate the queries using methods from social choice theory. We study ranking queries in diagnostic settings like medical and fault diagnosis and discuss how the Partial Borda Choice function from the literature can be applied to merge multiple query results. We discuss some additional interesting properties in our setting and evaluate the robustness of our approach empirically.}, author = {Potyka, Nico and Zhu, Yuqicheng and He, Yunjie and Kharlamov, Evgeny and Staab, Steffen}, booktitle = {In Proceedings of the 23rd International Conference on Autonomous Agents and Multi-Agent Systems}, title = {Robust Knowledge Extraction from Large Language Models using Social Choice Theory}, url = {https://arxiv.org/abs/2312.14877}, year = 2024 }
31. Schwindt, S., Meisinger, L., Negreiros, B., Schneider, T., & Nowak, W. (2024). Transfer learning achieves high recall for object classification in fluvial environments with limited data. Geomorphology, 455, 109185. https://doi.org/10.1016/j.geomorph.2024.109185
  - BibTeX
  BibTeX
  @inproceedings{SCHWINDT2024109185, abstract = {Field surveys to collect data from fluvial ecosystems traditionally focus on specific phenomena related to geomorphology or hydrology. Low-cost unmanned aerial vehicles (UAVs) additionally empower the fast and massive collection of airborne photogrammetry, providing geospatially explicit information. This remote sensing data complements field surveys by offering contextual information on geomorphological conditions, including digital terrain models. AI-based image recognition can augment contextual information to extrapolate archetypal object classes through name labels, such as “gravel”, “sand”, “plant”, or “large wood”. However, obtaining sufficient ground truth data for these classifications, particularly in morphodynamic fluvial environments, is challenging and induces high costs. This study introduces a transfer learning approach to address the challenge of low data availability, enabling AI-based mapping of complex objects in fluvial landscapes. We leverage the learned general structure of a deep convolutional neural network (CNN) pre-trained on a broad range of images. The fixed latent features of the pre-trained CNN stem from GoogLeNet. A fixed feature extractor serves to classify objects with limited data amounts. Satisfactory performance is measured with a recall rate, expressing the ability of a model to find all occurrences of a class on an image. High spatial heterogeneity in the locations of measurements on the x-y plane improves model performance. With a minimum of 400 labeled instances, the model achieves a satisfactory 93.75-% recall for a “large wood” target class, providing evidence of the effectiveness of transfer learning in remote sensing for geomorphological studies. This ability to detect large woods in river environments is critical to restoration efforts as it helps create fish habitat, which is essential to supporting biodiversity.}, author = {Schwindt, Sebastian and Meisinger, Lisa and Negreiros, Beatriz and Schneider, Tim and Nowak, Wolfgang}, doi = {https://doi.org/10.1016/j.geomorph.2024.109185}, issn = {0169-555X}, journal = {Geomorphology}, language = {en}, pages = 109185, title = {Transfer learning achieves high recall for object classification in fluvial environments with limited data}, url = {https://www.sciencedirect.com/science/article/pii/S0169555X24001351}, volume = 455, year = 2024 }
32. Seifer, P., Hernández, D., Lämmel, R., & Staab, S. (2024). Code for From Shapes to Shapes. https://doi.org/10.18419/darus-3977
  - BibTeX
  BibTeX
  @misc{seifer2024shapes, abstract = {This dataset contains the implementation code for an algorithm to infer SHACL shapes that the graph returned by an SPARQL CONSTRUCT query must satisfy if the input satisfies a given set of SHACL shapes. This dataset also includes an evaluation for the algorithm. The algorithm implemented in this dataset is proposed in the paper From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries. To execute the code, follow the instructions in the README.md file. For more info, please check the paper, and please have no hesitation to contact the authors for any inquiries. }, affiliation = {Seifer, Philipp/University of Koblenz, Hernández, Daniel/University of Stuttgart, Lämmel, Ralf/University of Koblenz, Staab, Steffen/University of Stuttgart}, author = {Seifer, Philipp and Hernández, Daniel and Lämmel, Ralf and Staab, Steffen}, doi = {10.18419/darus-3977}, howpublished = {Software}, note = {Related to: Philipp Seifer, Daniel Hernández, Ralf Lämmel, and Steffen Staab. 2024. From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries. In Proceedings of the ACM Web Conference 2024 (WWW ’24). ACM. doi: 10.1145/3589334.3645550}, orcid-numbers = {Seifer, Philipp/0000-0002-7421-2060, Hernández, Daniel/0000-0002-7896-0875, Lämmel, Ralf/0000-0001-9946-4363, Staab, Steffen/0000-0002-0780-4154}, title = {Code for From Shapes to Shapes}, year = 2024 }
33. Serra, G., & Niepert, M. (2024). L2XGNN: Learning to Explain Graph Neural Networks. Machine Learning Journal. https://arxiv.org/abs/2209.14402
  - BibTeX
  BibTeX
  @inproceedings{serra2024l2xgnnlearningexplaingraph, abstract = {Graph Neural Networks (GNNs) are a popular class of machine learning models. Inspired by the learning to explain (L2X) paradigm, we propose L2XGNN, a framework for explainable GNNs that provides faithful explanations by design. L2XGNN learns a mechanism for selecting explanatory subgraphs (motifs), which are exclusively used in the GNN message-passing operations. L2XGNN can select, for each input graph, a subgraph with specific properties, such as being sparse and connected. Imposing such constraints on the motifs often leads to more interpretable and effective explanations. Experiments on several datasets suggest that L2XGNN achieves the same classification accuracy as baseline methods using the entire input graph while ensuring that only the provided explanations are used to make predictions. Moreover, we show that L2XGNN can identify motifs responsible for the graph's properties it is intended to predict.}, archiveprefix = {arXiv}, author = {Serra, Giuseppe and Niepert, Mathias}, booktitle = {Proceedings of the European Conference on Machine Learning(ECML 2024)}, eprint = {2209.14402}, journal = {Machine Learning Journal}, language = {en}, primaryclass = {cs.LG}, title = {L2XGNN: Learning to Explain Graph Neural Networks}, url = {https://arxiv.org/abs/2209.14402}, year = 2024 }
34. Tan, Y., Lv, H., Zhou, Z., Guo, W., Xiong, B., Liu, W., Chen, C., Wang, S., & Yang, C. (2024). Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation. The 40th IEEE International Conference on Data Engineering. http://www.cs.emory.edu/~jyang71/files/logirec.pdf
  - BibTeX
  BibTeX
  @article{Yanchao2024LogicR, abbr = {ICDE}, author = {Tan, Yanchao and Lv, Hang and Zhou, Zihao and Guo, Wenzhong and Xiong, Bo and Liu, Weiming and Chen, Chaochao and Wang, Shiping and Yang, Carl}, journal = {The 40th IEEE International Conference on Data Engineering}, title = {Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation}, url = {http://www.cs.emory.edu/~jyang71/files/logirec.pdf}, year = 2024 }
35. Xiong, B., Nayyeri, M., Cochez, M., & Staab, S. (2024). Code for Hyperbolic Embedding Inference for Structured Multi-Label Prediction. DaRUS. https://doi.org/10.18419/DARUS-3988
  - BibTeX
  BibTeX
  @dataset{https://doi.org/10.18419/darus-3988, abstract = {This is a PyTorch implementation of the paper Hyperbolic Embedding Inference for Structured Multi-Label Prediction published in NeurIPS 2022. The code provides the Python scripts to reproduce the experiments in the paper, as well as a proof-of-concept example of the method. To execute the code, follow the instructions in the README.md file. For more info, please check the paper. Please have no hesitation to contact the authors for any inquiries.}, author = {Xiong, Bo and Nayyeri, Mojtaba and Cochez, Michael and Staab, Steffen}, doi = {10.18419/DARUS-3988}, publisher = {DaRUS}, title = {Code for Hyperbolic Embedding Inference for Structured Multi-Label Prediction}, url = {https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/darus-3988}, year = 2024 }
36. Xiong, B., Nayyeri, M., Luo, L., Wang, Z., Pan, S., & Staab, S. (2024). NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning. The 38th Annual AAAI Conference on Artificial Intelligence. https://arxiv.org/abs/2312.09219
  - BibTeX
  BibTeX
  @inproceedings{xiong_neste_aaai24, author = {Xiong, Bo and Nayyeri, Mojtaba and Luo, Linhao and Wang, Zihao and Pan, Shirui and Staab, Steffen}, booktitle = {The 38th Annual AAAI Conference on Artificial Intelligence}, title = {NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning}, url = {https://arxiv.org/abs/2312.09219}, year = 2024 }
37. Xiong, B., Nayyeri, M., Luo, L., Wang, Z., Pan, S., & Staab, S. (2024). Replication Data for NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning (AAAI’24). DaRUS. https://doi.org/10.18419/DARUS-3978
  - BibTeX
  BibTeX
  @dataset{https://doi.org/10.18419/darus-3978, abstract = {This code is a PyTorch implementation of the paper "NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning (AAAI'24)".NestE is a knowledge graph embedding method that can encode nested facts represented by quoted triples (h,r,t) in which the subject and object are triples themselves, e.g., ((BarackObama, holds_position, President), succeed_by, (DonaldTrump, holds_position, President)).We implement six variant models of NetsE based on different hypercomplex number systems. NestE_Q.py for NestE with quaternion. NestE_H.py for NestE with hyperbolic quaternion. NestE_D.py for NestE with split quaternion. NestE_B.py, NestE_HB.py, and NestE_DB.py are the respective version with a translation component. This code is used to reproduce the experiments of the paper. To execute the code, follow the instructions in the README.md file.}, author = {Xiong, Bo and Nayyeri, Mojtaba and Luo, Linhao and Wang, Zihao and Pan, Shirui and Staab, Steffen}, doi = {10.18419/DARUS-3978}, publisher = {DaRUS}, title = {Replication Data for NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning (AAAI'24)}, url = {https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/darus-3978}, year = 2024 }
38. Xiong, B., Nayyeri, M., Pan, S., & Staab, S. (2024). Code for Shrinking Embeddings for Hyper-relational Knowledge Graphs. DaRUS. https://doi.org/10.18419/DARUS-3979
  - BibTeX
  BibTeX
  @dataset{https://doi.org/10.18419/darus-3979, abstract = {This is a Pytorch implementation of the paper Shrinking Embeddings for Hyper-relational Knowledge Graphs published in ACL'23.This code is used to reproduce the experiments of the method ShrinkE, a geometric embedding approach for hyper-relational knowledge graphs. The code is implemented with Python 3 and pytorch. The code is tested on public datasets which can be download from StarE. To execute the code, follow the instructions in the README.md file. For more info, please check the paper or feel free to contact the authors for any inquiries.}, author = {Xiong, Bo and Nayyeri, Mojtaba and Pan, Shirui and Staab, Steffen}, doi = {10.18419/DARUS-3979}, publisher = {DaRUS}, title = {Code for Shrinking Embeddings for Hyper-relational Knowledge Graphs}, url = {https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/darus-3979}, year = 2024 }
39. Xiong, B., Potyka, N., Tran, T.-K., Nayyeri, M., & Staab, S. (2024). Code for Faithful Embeddings for EL++ Knowledge Bases. DaRUS. https://doi.org/10.18419/DARUS-3989
  - BibTeX
  BibTeX
  @dataset{https://doi.org/10.18419/darus-3989, author = {Xiong, Bo and Potyka, Nico and Tran, Trung-Kien and Nayyeri, Mojtaba and Staab, Steffen}, doi = {10.18419/DARUS-3989}, publisher = {DaRUS}, title = {Code for Faithful Embeddings for EL++ Knowledge Bases}, url = {https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/darus-3989}, year = 2024 }
40. Xiong, B., Zhu, S., Nayyeri, M., Xu, C., Pan, S., & Staab, S. (2024). Code for Ultrahyperbolic Knowledge Graph Embeddings. DaRUS. https://doi.org/10.18419/DARUS-4342
  - BibTeX
  BibTeX
  @dataset{https://doi.org/10.18419/darus-4342, abstract = {This is a Pytorch implementation of the paper Ultrahyperbolic Knowledge Graph Embeddings published in KDD 2022. This code is used to reproduce the experiments of the method UltraE, a geometric embedding approach for knowledge graph embeddings. The code is tested on public datasets which can be downloaded from KGEmb. To execute the code, follow the instructions in the README.md file. For more info, please check the paper or feel free to contact the authors for any inquiries.}, author = {Xiong, Bo and Zhu, Shichao and Nayyeri, Mojtaba and Xu, Chengjin and Pan, Shirui and Staab, Steffen}, doi = {10.18419/DARUS-4342}, publisher = {DaRUS}, title = {Code for Ultrahyperbolic Knowledge Graph Embeddings}, url = {https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/darus-4342}, year = 2024 }
41. Zaverkin, V., Holzmüller, D., Christiansen, H., Errica, F., Alesiani, F., Takamoto, M., Niepert, M., & Kästner, J. (2024). Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials. Npj Comput. Mater., 10(1), Article 1. https://doi.org/10.1038/s41524-024-01254-1
  - BibTeX
  BibTeX
  @article{zaverkin2024uncertaintybiased, abstract = {Efficiently creating a concise but comprehensive data set for training machine-learned interatomic potentials (MLIPs) is an under-explored problem. Active learning, which uses biased or unbiased molecular dynamics (MD) to generate candidate pools, aims to address this objective. Existing biased and unbiased MD-simulation methods, however, are prone to miss either rare events or extrapolative regions—areas of the configurational space where unreliable predictions are made. This work demonstrates that MD, when biased by the MLIP’s energy uncertainty, simultaneously captures extrapolative regions and rare events, which is crucial for developing uniformly accurate MLIPs. Furthermore, exploiting automatic differentiation, we enhance bias-forces-driven MD with the concept of bias stress. We employ calibrated gradient-based uncertainties to yield MLIPs with similar or, sometimes, better accuracy than ensemble-based methods at a lower computational cost. Finally, we apply uncertainty-biased MD to alanine dipeptide and MIL-53(Al), generating MLIPs that represent both configurational spaces more accurately than models trained with conventional MD.}, author = {Zaverkin, Viktor and Holzmüller, David and Christiansen, Henrik and Errica, Federico and Alesiani, Francesco and Takamoto, Makoto and Niepert, Mathias and Kästner, Johannes}, doi = {10.1038/s41524-024-01254-1}, issn = {20573960}, journal = {npj Comput. Mater.}, number = 1, pages = {83--}, publisher = {Springer Nature}, refid = {Zaverkin2024}, title = {Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials}, type = {Publication}, url = {https://doi.org/10.1038/s41524-024-01254-1}, volume = 10, year = 2024 }
42. Zhu, Y., Potyka, N., Nayyeri, M., Xiong, B., He, Y., Kharlamov, E., & Staab, S. (2024). Predictive Multiplicity of Knowledge Graph Embeddings in Link Prediction. Proceeding of the 2024 Conference on Empirical Methods in Natural Language Processing. https://arxiv.org/pdf/2408.08226
  - BibTeX
  BibTeX
  @inproceedings{zhu2024predictive, abstract = {Knowledge graph embedding (KGE) models are often used to predict missing links for knowledge graphs (KGs). However, multiple KG embeddings can perform almost equally well for link prediction yet give conflicting predictions for unseen queries. This phenomenon is termed \textit{predictive multiplicity} in the literature. It poses substantial risks for KGE-based applications in high-stake domains but has been overlooked in KGE research. We define predictive multiplicity in link prediction, introduce evaluation metrics and measure predictive multiplicity for representative KGE methods on commonly used benchmark datasets. Our empirical study reveals significant predictive multiplicity in link prediction, with 8% to 39% testing queries exhibiting conflicting predictions. We address this issue by leveraging voting methods from social choice theory, significantly mitigating conflicts by 66% to 78% in our experiments.}, author = {Zhu, Yuqicheng and Potyka, Nico and Nayyeri, Mojtaba and Xiong, Bo and He, Yunjie and Kharlamov, Evgeny and Staab, Steffen}, booktitle = {Proceeding of the 2024 Conference on Empirical Methods in Natural Language Processing}, title = {Predictive Multiplicity of Knowledge Graph Embeddings in Link Prediction}, url = {https://arxiv.org/pdf/2408.08226}, year = 2024 }
2023
1. Baier, A., Aspandi, D., & Staab, S. (2023, August). ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks. Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23. https://opencms.uni-stuttgart.de/permalink/7e4a70e2-10ca-11ee-ba91-000e0c3db68b.pdf
  - BibTeX
  BibTeX
  @inproceedings{Baier2023, abstract = {Multistep prediction models are essential for the simulation and model-predictive control of dynamical systems. Verifying the safety of such models is a multi-faceted problem requiring both system-theoretic guarantees as well as establishing trust with human users. In this work, we propose a novel approach, ReLiNet (Recurrent Linear Parameter Varying Network), to ensure safety for multistep prediction of dynamical systems. Our approach simplifies a recurrent neural network to a switched linear system that is constrained to guarantee exponential stability, which acts as a surrogate for safety from a system-theoretic perspective. Furthermore, ReLiNet’s computation can be reduced to a single linear model for each time step, resulting in predictions that are explainable by definition, thereby establishing trust from a human-centric perspective. Our quantitative experiments show that ReLiNet achieves prediction accuracy comparable to that of state-of-the-art recurrent neural networks, while achieving more faithful and robust explanations compared to the model-agnostic explanation method of LIME.}, author = {Baier, Alexandra and Aspandi, Decky and Staab, Steffen}, booktitle = {Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23}, month = {08}, note = {Main Track}, publisher = {International Joint Conferences on Artificial Intelligence Organization}, title = {ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks}, url = {https://opencms.uni-stuttgart.de/permalink/7e4a70e2-10ca-11ee-ba91-000e0c3db68b.pdf}, year = 2023 }
2. Hedeshy, R., Menges, R., & Staab, S. (2023). CNVVE: Dataset and Benchmark for Classifying Non-verbal Voice Expressions. Proc. INTERSPEECH 2023, 1553–1557. https://doi.org/10.21437/Interspeech.2023-201
  - BibTeX
  BibTeX
  @inproceedings{hedeshy2023cnvve, abstract = {Non-verbal voice expressions (NVVEs) have been adopted as a means of human-computer interaction in research studies. However, exploring non-verbal voice-based interactions has been constrained by the limited availability of suitable training data and computational methods for classifying such expressions, leading to a focus on simple binary inputs. We address this issue with a new dataset containing 950 audio samples comprising 6 classes of voice expressions. The data were collected from 42 speakers who donated voice recordings. The classifier was trained on the data using features derived from mel-spectrograms. Furthermore, we studied the effectiveness of data augmentation and improved over the baseline model accuracy significantly with a test accuracy of 96.6% in a 5-fold cross-validation. We have made CNVVE publicly accessible in the hope that it will serve as a benchmark for future research.}, author = {Hedeshy, Ramin and Menges, Raphael and Staab, Steffen}, booktitle = {Proc. INTERSPEECH 2023}, doi = {10.21437/Interspeech.2023-201}, eventdate = {August 20-24}, eventtitle = {Interspeech 2023}, month = {08}, pages = {1553-1557}, title = {CNVVE: Dataset and Benchmark for Classifying Non-verbal Voice Expressions}, url = {https://www.isca-speech.org/archive/interspeech_2023/hedeshy23_interspeech.html}, year = 2023 }
3. Liu, A., Niepert, M., & den Broeck, G. V. (2023, May). Image Inpainting via Tractable Steering of Diffusion Models. Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7--11, 2024, Austria. https://doi.org/10.48550/arXiv.2401.03349
  - BibTeX
  BibTeX
  @inproceedings{liu2023image, abstract = {Diffusion models are the current state of the art for generating photorealistic images. However, controlling the sampling process for constrained image generation tasks such as inpainting remains challenging since exact conditioning on such constraints is intractable. While existing methods use various techniques to approximate the constrained posterior, this paper proposes to exploit the ability of Tractable Probabilistic Models (TPMs) to exactly and efficiently compute the constrained posterior, and to leverage this signal to steer the denoising process of diffusion models. Specifically, this paper adopts a class of expressive TPMs termed Probabilistic Circuits (PCs). Building upon prior advances, we further scale up PCs and make them capable of guiding the image generation process of diffusion models. Empirical results suggest that our approach can consistently improve the overall quality and semantic coherence of in painted images across three natural image datasets (i.e., CelebA-HQ, ImageNet, and LSUN) with only ~10% additional computational overhead brought by the TPM.}, archiveprefix = {arXiv}, author = {Liu, Anji and Niepert, Mathias and den Broeck, Guy Van}, booktitle = {Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7--11, 2024, Austria}, doi = {10.48550/arXiv.2401.03349}, eprint = {2401.03349}, language = {en}, month = {05}, preprinturl = {https://arxiv.org/abs/2401.03349v1}, primaryclass = {cs.CV}, publisher = {ICLR}, title = {Image Inpainting via Tractable Steering of Diffusion Models}, venue = {Austria}, year = 2023 }
4. Baier, A., Aspandi, D., & Staab, S. (2023). Supplements for “ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks”". DaRUS. https://doi.org/10.18419/DARUS-3457
  - BibTeX
  BibTeX
  @dataset{https://doi.org/10.18419/darus-3457, abstract = {This repository contains the necessary scripts to reproduce the results from our paper "ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks". See the README file for more information. The most current version of this software is available on Github.}, author = {Baier, Alexandra and Aspandi, Decky and Staab, Steffen}, doi = {10.18419/DARUS-3457}, publisher = {DaRUS}, title = {Supplements for "ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks""}, url = {https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/darus-3457}, year = 2023 }
5. Baier, A., & Frank, D. (2023). deepsysid: System Identification Toolkit for Multistep Prediction using Deep Learning. DaRUS. https://doi.org/10.18419/DARUS-3455
  - BibTeX
  BibTeX
  @dataset{https://doi.org/10.18419/darus-3455, abstract = {deepsysid is a system identification toolkit for multistep prediction using deep learning and hybrid methods. The toolkit is easy to use. After you follow the instructions in the README, you will be able to download a dataset, run hyperparameter optimization and identify your best-performing multistep prediction models with just three commands. The most current version of this software is available on GitHub.}, author = {Baier, Alexandra and Frank, Daniel}, doi = {10.18419/DARUS-3455}, publisher = {DaRUS}, title = {deepsysid: System Identification Toolkit for Multistep Prediction using Deep Learning}, url = {https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/darus-3455}, year = 2023 }
6. Elshani, D., Hernandez, D., Lombardi, A., Siriwardena, L., Schwinn, T., Fisher, A., Staab, S., Menges, A., & Wortmann, T. (2023). Building Information Validation and Reasoning Using Semantic Web Technologies. In M. Turrin, C. Andriotis, & A. Rafiee (Eds.), Computer-Aided Architectural Design. INTERCONNECTIONS: Co-computing Beyond Boundaries (pp. 470--484). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-37189-9_31
  - BibTeX
  BibTeX
  @inproceedings{10.1007/978-3-031-37189-9_31, abstract = {The integration of data from various disciplines, including requirements and regulations, is essential in the co-design of buildings. Constraints that arise during design, fabrication, or construction are mainly considered in the later stages of the design process. This often leads to costly revisions during fabrication or construction. While there are some works proposing Semantic Web approaches or ad-hoc methods to identify constraint violations in the early stages of design, they mainly rely on data derived from monolithic data schemas. This paper presents a Semantic Web approach that validates and checks building data derived from federated data schemas. The proposed approach is exemplified through the design process of a segmented timber shell, a complex structure requiring negotiations across architectural, engineering, and fabrication parameters. The Building Habitat object Models (BHoM) framework is used to represent federated building data, and the approach enables a seamless movement between the graph environment and object models to support the design process. Additionally, the study discusses various technologies that aid in knowledge inference or data validation. The results of this study suggest that the use of Semantic Web technologies in conjunction with federated data schemas has significant potential to enhance co-design processes in the building industry. While further research is needed to assess its effectiveness in other scenarios, our application of this approach in checking and validating building data for a complex segmented timber shell structure demonstrates its promise as a means to streamline the design process.}, address = {Cham}, author = {Elshani, Diellza and Hernandez, Daniel and Lombardi, Alessio and Siriwardena, Lasath and Schwinn, Tobias and Fisher, Al and Staab, Steffen and Menges, Achim and Wortmann, Thomas}, booktitle = {Computer-Aided Architectural Design. INTERCONNECTIONS: Co-computing Beyond Boundaries}, doi = {10.1007/978-3-031-37189-9_31}, editor = {Turrin, Michela and Andriotis, Charalampos and Rafiee, Azarakhsh}, isbn = {978-3-031-37189-9}, pages = {470--484}, publisher = {Springer Nature Switzerland}, title = {Building Information Validation and Reasoning Using Semantic Web Technologies}, url = {https://doi.org/10.1007/978-3-031-37189-9_31}, year = 2023 }
7. Elshani, D., Lombardi, A., Hernández, D., Staab, S., Fisher, A., & Wortmann, T. (2023). BHoM to bhOWL converter. DaRUS. https://doi.org/10.18419/darus-3364
  - BibTeX
  BibTeX
  @dataset{darus-3364_2023, author = {Elshani, Diellza and Lombardi, Alessio and Hernández, Daniel and Staab, Steffen and Fisher, Al and Wortmann, Thomas}, doi = {10.18419/darus-3364}, publisher = {DaRUS}, title = {BHoM to bhOWL converter}, url = {https://doi.org/10.18419/darus-3364}, version = {V1}, year = 2023 }
8. Galárraga, L., Hernández, D., Katim, A., & Hose, K. (2023). Visualizing How-Provenance Explanations for SPARQL Queries. In Y. Ding, J. Tang, J. F. Sequeda, L. Aroyo, C. Castillo, & G.-J. Houben (Eds.), Companion Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023 (pp. 212–216). ACM. https://doi.org/10.1145/3543873.3587350
  - BibTeX
  BibTeX
  @inproceedings{DBLP:conf/www/Galarraga0KH23, abstract = {Knowledge graphs (KGs) are vast collections of machine-readable information, usually modeled in RDF and queried with SPARQL. KGs have opened the door to a plethora of applications such as Web search or smart assistants that query and process the knowledge contained in those KGs. An important, but often disregarded, aspect of querying KGs is query provenance: explanations of the data sources and transformations that made a query result possible. In this article we demonstrate, through a Web application, the capabilities of SPARQLprov, an engine-agnostic method that annotates query results with how-provenance annotations. To this end, SPARQLprov resorts to query rewriting techniques, which make it applicable to already deployed SPARQL endpoints. We describe the principles behind SPARQLprov and discuss perspectives on visualizing how-provenance explanations for SPARQL queries. }, author = {Galárraga, Luis and Hernández, Daniel and Katim, Anas and Hose, Katja}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {Companion Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023}, doi = {10.1145/3543873.3587350}, editor = {Ding, Ying and Tang, Jie and Sequeda, Juan F. and Aroyo, Lora and Castillo, Carlos and Houben, Geert-Jan}, eventdate = {30 April 2023 - 4 May 2023}, eventtitle = {ACM Web Conference 2023, WWW 2023}, language = {English}, pages = {212-216}, publisher = {ACM}, title = {Visualizing How-Provenance Explanations for SPARQL Queries}, url = {https://doi.org/10.1145/3543873.3587350}, venue = {Austin, Texas, USA}, year = 2023 }
9. Gregucci, C. (2023). Query Answering over the Polymorphic Web of Data. In C. Pesquita, H. Skaf-Molli, V. Efthymiou, S. Kirrane, A. Ngonga, D. Collarana, R. Cerqueira, M. Alam, C. Trojahn, & S. Hertling (Eds.), The Semantic Web: ESWC 2023 Satellite Events - Hersonissos, Crete, Greece, May 28 - June 1, 2023, Proceedings (Vol. 13998, pp. 255--265). Springer. https://doi.org/10.1007/978-3-031-43458-7_44
  - BibTeX
  BibTeX
  @inproceedings{DBLP:conf/esws/Gregucci23, author = {Gregucci, Cosimo}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {The Semantic Web: {ESWC} 2023 Satellite Events - Hersonissos, Crete, Greece, May 28 - June 1, 2023, Proceedings}, doi = {10.1007/978-3-031-43458-7_44}, editor = {Pesquita, Catia and Skaf{-}Molli, Hala and Efthymiou, Vasilis and Kirrane, Sabrina and Ngonga, Axel and Collarana, Diego and Cerqueira, Renato and Alam, Mehwish and Trojahn, C{\'{a}}ssia and Hertling, Sven}, pages = {255--265}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Query Answering over the Polymorphic Web of Data}, url = {https://doi.org/10.1007/978-3-031-43458-7_44}, volume = 13998, year = 2023 }
10. Gregucci, C., Nayyeri, M., Hernández, D., & Staab, S. (2023). Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models. In Y. Ding, J. Tang, J. F. Sequeda, L. Aroyo, C. Castillo, & G.-J. Houben (Eds.), Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023 (pp. 2600–2610). ACM. https://doi.org/10.1145/3543507.3583358
  - BibTeX
  BibTeX
  @inproceedings{gregucci2023link, abstract = {Predicting missing links between entities in a knowledge graph is a fundamental task to deal with the incompleteness of data on the Web. Knowledge graph embeddings map nodes into a vector space to predict new links, scoring them according to geometric criteria. Relations in the graph may follow patterns that can be learned, e.g., some relations might be symmetric and others might be hierarchical. However, the learning capability of different embedding models varies for each pattern and, so far, no single model can learn all patterns equally well. In this paper, we combine the query representations from several models in a unified one to incorporate patterns that are independently captured by each model. Our combination uses attention to select the most suitable model to answer each query. The models are also mapped onto a non-Euclidean manifold, the Poincaré ball, to capture structural patterns, such as hierarchies, besides relational patterns, such as symmetry. We prove that our combination provides a higher expressiveness and inference power than each model on its own. As a result, the combined model can learn relational and structural patterns. We conduct extensive experimental analysis with various link prediction benchmarks showing that the combined model outperforms individual models, including state-of-the-art approaches.}, archiveprefix = {arXiv}, author = {Gregucci, Cosimo and Nayyeri, Mojtaba and Hernández, Daniel and Staab, Steffen}, booktitle = {Proceedings of the {ACM} Web Conference 2023, {WWW} 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023}, doi = {10.1145/3543507.3583358}, editor = {Ding, Ying and Tang, Jie and Sequeda, Juan F. and Aroyo, Lora and Castillo, Carlos and Houben, Geert-Jan}, eprint = {2302.06229}, eventdate = {30 April 2023 - 4 May 2023}, eventtitle = {ACM Web Conference 2023}, language = {English}, pages = {2600-2610}, preprinturl = {https://doi.org/10.48550/arXiv.2302.06229}, primaryclass = {cs.AI}, publisher = {ACM}, title = {Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models}, url = {https://doi.org/10.1145/3543507.3583358}, venue = {Austin, Texas, USA}, year = 2023 }
11. He, Y., Nayyeri, M., Xiong, B., Zhu, Y., Kharlamov, E., & Staab, S. (2023). Can Pattern Learning Enhance Complex Logical Query Answering? CEUR Workshop Proceedings. https://hozo.jp/ISWC2023_PD-Industry-proc/ISWC2023_paper_463.pdf
  - BibTeX
  BibTeX
  @inproceedings{he2023pattern, abstract = {Logical patterns, such as symmetry and composition, have been proven to be beneficial in the knowledge graph completion task. However, their influence has been unexplored in first-order logical (FOL) query reasoning methods. In this work, we present an inductive bias for query embedding models, Patternaware Cone Embedding (PConE), to support learning and reasoning with logical patterns. PConE combines the advantages of cones and the rotation operator for powerful algebraic operations for pattern inference. Our experiments demonstrate how the capability to capture logical patterns positively impacts the results of query answering.}, author = {He, Yunjie and Nayyeri, Mojtaba and Xiong, Bo and Zhu, Yuqicheng and Kharlamov, Evgeny and Staab, Steffen}, booktitle = {CEUR Workshop Proceedings}, title = {Can Pattern Learning Enhance Complex Logical Query Answering?}, url = {https://hozo.jp/ISWC2023_PD-Industry-proc/ISWC2023_paper_463.pdf}, venue = {Thhe 22nd International Semantic Web Conference (ISWC 2023 posters and demos)}, year = 2023 }
12. Hosseini, A. S., & Staab, S. (2023). Emotional Framing in the Spreading of False and True Claims. Proceedings of the 15th ACM Web Science Conference 2023, 96--106.
  - BibTeX
  BibTeX
  @inproceedings{hosseini2023emotional, author = {Hosseini, Akram Sadat and Staab, Steffen}, booktitle = {Proceedings of the 15th ACM Web Science Conference 2023}, pages = {96--106}, title = {Emotional Framing in the Spreading of False and True Claims}, year = 2023 }
13. Lu, J., Shen, J., Xiong, B., Ma, W., Staab, S., & Yang, C. (2023). HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting. The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval 2023. https://arxiv.org/abs/2304.05973
  - BibTeX
  BibTeX
  @conference{lu2023hiprompt, author = {Lu, Jiaying and Shen, Jiaming and Xiong, Bo and Ma, Wenjing and Staab, Steffen and Yang, Carl}, booktitle = {The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval 2023}, title = {HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting}, url = {https://arxiv.org/abs/2304.05973}, year = 2023 }
14. Minh Ho Nguyen, D., Ngoc Pham, T., Tuong Diep, N., Phan, N., Pham, Q., Tong, V., T. Nguyen, B., Hoang Le, N., Ho, N., Xie, P., Sonntag, D., & Niepert, M. (2023). On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation. Advances in Neural Information Processing Systems (NeurIPS), Workshop on Robustness of Zero/Few-Shot Learning in Foundation Models. https://arxiv.org/pdf/2311.11096.pdf
  - BibTeX
  BibTeX
  @conference{minhhonguyen2023distribution, author = {Minh Ho Nguyen, Duy and Ngoc Pham, Tan and Tuong Diep, Nghiem and Phan, Nghi and Pham, Quang and Tong, Vinh and T. Nguyen, Binh and Hoang Le, Ngan and Ho, Nhat and Xie, Pengtao and Sonntag, Daniel and Niepert, Mathias}, booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Workshop on robustness of zero/few-shot learning in foundation models}, title = {On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation}, url = {https://arxiv.org/pdf/2311.11096.pdf}, year = 2023 }
15. Minh Ho Nguyen, D., Nguyen, H., N. T. Mai, T., Tri, C., T. Nguyen, B., Ho, N., Swoboda, P., Albarqouni, S., Xie, P., & Sonntag, D. (2023). Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering. Proceedings of the AAAI Conference on Artificial Intelligence. https://ojs.aaai.org/index.php/AAAI/article/view/26687
  - BibTeX
  BibTeX
  @conference{mhnguyen2023joint, author = {Minh Ho Nguyen, Duy and Nguyen, Hoang and N. T. Mai, Truong and Tri, Cao and T. Nguyen, Binh and Ho, Nhat and Swoboda, Paul and Albarqouni, Shadi and Xie, Pengtao and Sonntag, Daniel}, booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence}, title = {Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering}, url = {https://ojs.aaai.org/index.php/AAAI/article/view/26687}, year = 2023 }
16. Minh Ho Nguyen, D., Nguyen, H., T Diep, N., N Pham, T., Cao, T., T Nguyen, B., Swoboda, P., Ho, N., Albarqouni, S., Xie, P., Sonntag, D., & Niepert, M. (2023). LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching. Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023). https://proceedings.neurips.cc/paper_files/paper/2023/file/58cc11cda2a2679e8af5c6317aed0af8-Paper-Conference.pdf
  - BibTeX
  BibTeX
  @conference{mhnguyen2023lvmmed, author = {Minh Ho Nguyen, Duy and Nguyen, Hoang and T Diep, Nghiem and N Pham, Tan and Cao, Tri and T Nguyen, Binh and Swoboda, Paul and Ho, Nhat and Albarqouni, Shadi and Xie, Pengtao and Sonntag, Daniel and Niepert, Mathias}, booktitle = {Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023)}, title = {LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching}, url = {https://proceedings.neurips.cc/paper_files/paper/2023/file/58cc11cda2a2679e8af5c6317aed0af8-Paper-Conference.pdf}, year = 2023 }
17. Mjalled, A., Torres, E., & Mönnigmann, M. (2023). Reduced-order modeling framework using two-level neural networks. PAMM, n/a(n/a), Article n/a. https://doi.org/10.1002/pamm.202300061
  - BibTeX
  BibTeX
  @article{https://doi.org/10.1002/pamm.202300061, abstract = {Abstract Established reduced-order modeling (ROM) methods, for instance, Galerkin-projection, approximate the solution by linearly projecting high-dimensional spaces to a lower-dimensional space spanned by the reduced basis. However, the accuracy of these methods may be insufficient for complex and multiscale simulations due to the restriction to a linear space. Alternatively, autoencoders (AEs) can be used for nonlinear dimensionality reduction. We combine nonlinear dimensionality reduction techniques with time series prediction to build data-driven ROMs. The presented framework consists of two-level neural networks. The high-dimensional space is nonlinearly compressed in the first level using the encoder function of the AE. Subsequently, the temporal evolution of the latent vector is predicted in the second level. The original solution can be easily reconstructed using the decoder function of the AE. In comparison with the projection-based ROMs, for example, proper orthogonal decomposition (POD) with Galerkin projection, this framework allows naturally to include parameters in the prediction without nonlinear interpolation of the linear basis. We demonstrate the framework on a two-dimensional flow field simulation around circular bodies parameterized with the inlet fluid velocity.}, author = {Mjalled, Ali and Torres, Edgar and Mönnigmann, Martin}, doi = {https://doi.org/10.1002/pamm.202300061}, eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/pamm.202300061}, journal = {PAMM}, number = {n/a}, pages = {e202300061}, title = {Reduced-order modeling framework using two-level neural networks}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/pamm.202300061}, volume = {n/a}, year = 2023 }
18. Monninger, T., Schmidt, J., Rupprecht, J., Raba, D., Jordan, J., Frank, D., Staab, S., & Dietmayer, K. (2023). SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks. IEEE Robotics and Automation Letters, 1–8. https://doi.org/10.1109/LRA.2023.3234771
  - BibTeX
  BibTeX
  @article{10008009, abstract = {Understanding traffic scenes requires considering heterogeneous information about dynamic agents and the static infrastructure. In this work we propose SCENE, a methodology to encode diverse traffic scenes in heterogeneous graphs and to reason about these graphs using a heterogeneous Graph Neural Network encoder and task-specific decoders. The heterogeneous graphs, whose structures are defined by an ontology, consist of different nodes with type-specific node features and different relations with type-specific edge features. In order to exploit all the information given by these graphs, we propose to use cascaded layers of graph convolution. The result is an encoding of the scene. Task-specific decoders can be applied to predict desired attributes of the scene. Extensive evaluation on two diverse binary node classification tasks show the main strength of this methodology: despite being generic, it even manages to outperform task-specific baselines. The further application of our methodology to the task of node classification in various knowledge graphs shows its transferability to other domains.}, author = {Monninger, Thomas and Schmidt, Julian and Rupprecht, Jan and Raba, David and Jordan, Julian and Frank, Daniel and Staab, Steffen and Dietmayer, Klaus}, doi = {10.1109/LRA.2023.3234771}, issn = {2377-3766}, journal = {IEEE Robotics and Automation Letters}, pages = {1-8}, title = {SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks}, year = 2023 }
19. Monninger, T., Weber, A., & Staab, S. (2023). Semantic Map Learning of Traffic Light to Lane Assignment based on Motion Data. 25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022, Macau, China, October 8-12, 2022, 1583--1590. https://doi.org/10.1109/ITSC57777.2023.10422549
  - BibTeX
  BibTeX
  @inproceedings{DBLP:conf/itsc/MonningerWS23, author = {Monninger, Thomas and Weber, Andreas and Staab, Steffen}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {25th {IEEE} International Conference on Intelligent Transportation Systems, {ITSC} 2022, Macau, China, October 8-12, 2022}, doi = {10.1109/ITSC57777.2023.10422549}, pages = {1583--1590}, publisher = {{IEEE}}, title = {Semantic Map Learning of Traffic Light to Lane Assignment based on Motion Data}, url = {https://doi.org/10.1109/ITSC57777.2023.10422549}, year = 2023 }
20. Morales-Alvarez, W., Certad, N., Roitberg, A., Stiefelhagen, R., & Olaverri-Monreal, C. (2023). On Transferability of Driver Observation Models from Simulated to Real Environments in Autonomous Cars. 2023 IEEE International Conference on Intelligent Transportation Systems (ITSC).
  - BibTeX
  BibTeX
  @article{morales2023transferability, author = {Morales-Alvarez, Walter and Certad, Novel and Roitberg, Alina and Stiefelhagen, Rainer and Olaverri-Monreal, Cristina}, booktitle = {2023 IEEE International Conference on Intelligent Transportation Systems (ITSC)}, organization = {IEEE}, title = {On Transferability of Driver Observation Models from Simulated to Real Environments in Autonomous Cars}, year = 2023 }
21. Nayyeri, M., Wang, Z., Akter, Mst. M., Alam, M. M., Rony, M. R. A. H., Lehmann, J., & Staab, S. (2023). Integrating Knowledge Graph Embeddings and Pre-trained Language Models in Hypercomplex Spaces. In The Semantic Web – ISWC 2023. https://doi.org/10.1007/978-3-031-47240-4_21
  - BibTeX
  BibTeX
  @incollection{nayyeri2023integrating, author = {Nayyeri, Mojtaba and Wang, Zihao and Akter, Mst. Mahfuja and Alam, Mirza Mohtashim and Rony, Md Rashad Al Hasan and Lehmann, Jens and Staab, Steffen}, doi = {10.1007/978-3-031-47240-4_21}, isbn = {9783031472398}, issn = {0302-9743}, journal = {The Semantic Web – ISWC 2023}, title = {Integrating Knowledge Graph Embeddings and Pre-trained Language Models in Hypercomplex Spaces}, url = {http://dx.doi.org/10.1007/978-3-031-47240-4_21}, year = 2023 }
22. Potyka, N., Zhu, Y., He, Y., Kharlamov, E., & Staab, S. (2023). Robust Knowledge Extraction from Large Language Models using Social Choice Theory.
  - BibTeX
  BibTeX
  @conference{noauthororeditor2023robust, abstract = {Large-language models (LLMs) have the potential to support a wide range of applications like conversational agents, creative writing, text improvement, and general query answering. However, they are ill-suited for query answering in high-stake domains like medicine because they generate answers at random and their answers are typically not robust - even the same query can result in different answers when prompted multiple times. In order to improve the robustness of LLM queries, we propose using ranking queries repeatedly and to aggregate the queries using methods from social choice theory. We study ranking queries in diagnostic settings like medical and fault diagnosis and discuss how the Partial Borda Choice function from the literature can be applied to merge multiple query results. We discuss some additional interesting properties in our setting and evaluate the robustness of our approach empirically.}, author = {Potyka, Nico and Zhu, Yuqicheng and He, Yunjie and Kharlamov, Evgeny and Staab, Steffen}, title = {Robust Knowledge Extraction from Large Language Models using Social Choice Theory}, year = 2023 }
23. Qian, C., Manolache, A., Ahmed, K., Zeng, Z., den Broeck, G. V., Niepert, M., & Morris, C. (2023). Probabilistic Task-Adaptive Graph Rewiring. ICML 2023 Workshop on Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators. https://openreview.net/forum?id=YsHKrMPHE1
  - BibTeX
  BibTeX
  @inproceedings{qian2023probabilistic, author = {Qian, Chendi and Manolache, Andrei and Ahmed, Kareem and Zeng, Zhe and den Broeck, Guy Van and Niepert, Mathias and Morris, Christopher}, booktitle = {ICML 2023 Workshop on Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators}, title = {Probabilistic Task-Adaptive Graph Rewiring}, url = {https://openreview.net/forum?id=YsHKrMPHE1}, year = 2023 }
24. Schmidt, J., Jordan, J., Gritschneder, F., Monninger, T., & Dietmayer, K. (2023). Exploring Navigation Maps for Learning-Based Motion Prediction. IEEE International Conference on Robotics and Automation, ICRA 2023, London, UK, May 29 - June 2, 2023, 3539--3545. https://doi.org/10.1109/ICRA48891.2023.10160989
  - BibTeX
  BibTeX
  @inproceedings{DBLP:conf/icra/SchmidtJGMD23, author = {Schmidt, Julian and Jordan, Julian and Gritschneder, Franz and Monninger, Thomas and Dietmayer, Klaus}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {{IEEE} International Conference on Robotics and Automation, {ICRA} 2023, London, UK, May 29 - June 2, 2023}, doi = {10.1109/ICRA48891.2023.10160989}, pages = {3539--3545}, publisher = {{IEEE}}, title = {Exploring Navigation Maps for Learning-Based Motion Prediction}, url = {https://doi.org/10.1109/ICRA48891.2023.10160989}, year = 2023 }
25. Schmidt, J., Monninger, T., Jordan, J., & Dietmayer, K. (2023). LMR: Lane Distance-Based Metric for Trajectory Prediction. IEEE Intelligent Vehicles Symposium, IV 2023, Anchorage, AK, USA, June 4-7, 2023, 1--6. https://doi.org/10.1109/IV55152.2023.10186555
  - BibTeX
  BibTeX
  @inproceedings{DBLP:conf/ivs/SchmidtMJD23, author = {Schmidt, Julian and Monninger, Thomas and Jordan, Julian and Dietmayer, Klaus}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {{IEEE} Intelligent Vehicles Symposium, {IV} 2023, Anchorage, AK, USA, June 4-7, 2023}, doi = {10.1109/IV55152.2023.10186555}, pages = {1--6}, preprinturl = {https://arxiv.org/abs/2304.05869}, publisher = {{IEEE}}, title = {{LMR:} Lane Distance-Based Metric for Trajectory Prediction}, url = {https://doi.org/10.1109/IV55152.2023.10186555}, year = 2023 }
26. Schneider, T., Totounferoush, A., Nowak, W., & Staab, S. (2023). Probabilistic Regular Tree Priors for Scientific Symbolic Reasoning. https://arxiv.org/pdf/2306.08506.pdf
  - BibTeX
  BibTeX
  @preprint{schneider2023probabilistic, archiveprefix = {arXiv}, author = {Schneider, Tim and Totounferoush, Amin and Nowak, Wolfgang and Staab, Steffen}, eprint = {2306.08506}, primaryclass = {cs.LG}, title = {Probabilistic Regular Tree Priors for Scientific Symbolic Reasoning}, type = {Publication}, url = {https://arxiv.org/pdf/2306.08506.pdf}, year = 2023 }
27. Tanama, C., Peng, K., Marinov, Z., Stiefelhagen, R., & Roitberg, A. (2023). Quantized Distillation: Optimizing Driver Activity Recognition Models for Resource-Constrained Environments. 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
  - BibTeX
  BibTeX
  @article{tanama2023quantized, author = {Tanama, Calvin and Peng, Kunyu and Marinov, Zdravko and Stiefelhagen, Rainer and Roitberg, Alina}, booktitle = {2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, organization = {IEEE}, title = {Quantized Distillation: Optimizing Driver Activity Recognition Models for Resource-Constrained Environments}, year = 2023 }
28. Wang, Y., Dima, C., & Staab, S. (2023). WikiMed-DE: Constructing a Silver-Standard Dataset for German Biomedical Entity Linking using Wikipedia and Wikidata. The 4th Wikidata Workshop @ ISWC 2023. https://openreview.net/forum?id=5dQ7YDSYya
  - BibTeX
  BibTeX
  @inproceedings{wang2023novel, author = {Wang, Yi and Dima, Corina and Staab, Steffen}, booktitle = {The 4th Wikidata Workshop @ ISWC 2023}, title = {WikiMed-{DE}: Constructing a Silver-Standard Dataset for German Biomedical Entity Linking using Wikipedia and Wikidata}, url = {https://openreview.net/forum?id=5dQ7YDSYya}, year = 2023 }
29. Xiong, B., Nayyeri, M., Pan, S., & Staab, S. (2023). Shrinking Embeddings for Hyper-Relational Knowledge Graphs. The 61st Annual Meeting of the Association for Computational Linguistics. https://arxiv.org/abs/2306.02199
  - BibTeX
  BibTeX
  @article{bo2023shrinking, abbr = {ACL}, arxiv = {2306.02199}, author = {Xiong, Bo and Nayyeri, Mojtaba and Pan, Shirui and Staab, Steffen}, code = {https://github.com/xiongbo010/ShrinkE}, journal = {The 61st Annual Meeting of the Association for Computational Linguistics}, title = {Shrinking Embeddings for Hyper-Relational Knowledge Graphs}, url = {https://arxiv.org/abs/2306.02199}, year = 2023 }
30. Zhu, Y., Potyka, N., Xiong, B., Tran, T.-K., Nayyeri, M., Staab, S., & Kharlamov, E. (2023). Towards Statistical Reasoning with Ontology Embeddings. https://hozo.jp/ISWC2023_PD-Industry-proc/ISWC2023_paper_442.pdf
  - BibTeX
  BibTeX
  @conference{zhu2023towards, abstract = {The Description Logic (DL) EL is a lightweight DL that has a favorable trade-off between expressive power and reasoning complexity and has been widely used in many real-world applications. Statistical EL (SEL) extends EL by allowing conditional probabilities over axioms. Unlike other probabilistic DLs, the probabilistic semantics of SEL is statistical, meaning that probabilities express proportions in a population rather than subjective beliefs. One major challenge is that reasoning in SEL is ExpTimecomplete. To overcome this problem, we propose to use embeddings to perform approximate inference over SEL ontologies. This poster paper demonstrates the progress of the ongoing research, showcasing a demonstration through a simplified example, providing preliminary findings, and outlining the future work.}, author = {Zhu, Yuqicheng and Potyka, Nico and Xiong, Bo and Tran, Trung-Kien and Nayyeri, Mojtaba and Staab, Steffen and Kharlamov, Evgeny}, title = {Towards Statistical Reasoning with Ontology Embeddings}, url = {https://hozo.jp/ISWC2023_PD-Industry-proc/ISWC2023_paper_442.pdf}, venue = {Thhe 22nd International Semantic Web Conference (ISWC 2023)}, year = 2023 }
31. Zhu, Y., Tnani, M.-A., Jahnz, T., & Diepold, K. (2023). Active Transfer Prototypical Network: An Efficient Labeling Algorithm for Time-Series Data. Procedia Computer Science, 217, 1427–1436.
  - BibTeX
  BibTeX
  @article{zhu2023active, abstract = {The paucity of labeled data is a typical challenge in the automotive industry. Annotating time-series measurements requires solid domain knowledge and in-depth exploratory data analysis, which implies a high labeling effort. Conventional Active Learning (AL) addresses this issue by actively querying the most informative instances based on the estimated classification probability and retraining the model iteratively. However, the learning efficiency strongly relies on the initial model, resulting in the trade-of between the size of the initial dataset and the query number. This paper proposes a novel Few-Shot Learning (FSL)-based AL framework, which addresses the trade-of problem by incorporating a Prototypical Network (ProtoNet) in the AL iterations. The results show an improvement, on the one hand, in the robustness to the initial model and, on the other hand, in the learning efficiency of the ProtoNet through the active selection of the support set in each iteration. This framework was validated on UCI HAR/HAPT dataset and a real-world braking maneuver dataset. The learning performance significantly surpasses traditional AL algorithms on both datasets, achieving 90% classification accuracy with 10% and 5% labeling effort, respectively.}, author = {Zhu, Yuqicheng and Tnani, Mohamed-Ali and Jahnz, Timo and Diepold, Klaus}, journal = {Procedia computer science}, pages = {1427-1436}, title = {Active Transfer Prototypical Network: An Efficient Labeling Algorithm for Time-Series Data}, volume = 217, year = 2023 }
2022
1. Brad, F., Manolache, A., Burceanu, E., Barbalau, A., Ionescu, R. T., & Popescu, M. (2022). Rethinking the Authorship Verification Experimental Setups. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 5634--5643. https://doi.org/10.18653/v1/2022.emnlp-main.380
  - BibTeX
  BibTeX
  @inproceedings{brad-etal-2022-rethinking, abstract = {One of the main drivers of the recent advances in authorship verification is the PAN large-scale authorship dataset. Despite generating significant progress in the field, inconsistent performance differences between the closed and open test sets have been reported. To this end, we improve the experimental setup by proposing five new public splits over the PAN dataset, specifically designed to isolate and identify biases related to the text topic and to the author{'}s writing style. We evaluate several BERT-like baselines on these splits, showing that such models are competitive with authorship verification state-of-the-art methods. Furthermore, using explainable AI, we find that these baselines are biased towards named entities. We show that models trained without the named entities obtain better results and generalize better when tested on DarkReddit, our new dataset for authorship verification.}, address = {Abu Dhabi, United Arab Emirates}, author = {Brad, Florin and Manolache, Andrei and Burceanu, Elena and Barbalau, Antonio and Ionescu, Radu Tudor and Popescu, Marius}, booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing}, doi = {10.18653/v1/2022.emnlp-main.380}, month = {12}, pages = {5634--5643}, publisher = {Association for Computational Linguistics}, title = {Rethinking the Authorship Verification Experimental Setups}, url = {https://aclanthology.org/2022.emnlp-main.380}, year = 2022 }
2. Elshani, D., Lombardi, A., Fisher, A., Staab, S., Hernández, D., & Wortmann, T. (2022, September). Inferential Reasoning in Co-Design Using Semantic Web Standards alongside BHoM. Proceedings of 33. Forum Bauinformatik.
  - BibTeX
  BibTeX
  @inproceedings{elshani2022inferential, author = {Elshani, Diellza and Lombardi, Alessio and Fisher, Al and Staab, Steffen and Hernández, Daniel and Wortmann, Thomas}, booktitle = { Proceedings of 33. Forum Bauinformatik }, eventdate = {September 7-9, 2022}, month = {09}, preprinturl = {https://www.researchgate.net/publication/363367234_Inferential_Reasoning_in_Co-Design_Using_Semantic_Web_Standards_alongside_BHoM}, title = {Inferential Reasoning in Co-Design Using Semantic Web Standards alongside BHoM}, venue = {Munich, Germany}, year = 2022 }
3. Xiong, B., Zhu, S., Nayyeri, M., Xu, C., Pan, S., Zhou, C., & Staab, S. (2022, August). Ultrahyperbolic Knowledge Graph Embeddings. 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-22. https://doi.org/10.1145/3534678.3539333
  - BibTeX
  BibTeX
  @inproceedings{boxiong2022ultrahyperbolic, abstract = {Recent knowledge graph (KG) embeddings have been advanced by hyperbolic geometry due to its superior capability for representing hierarchies. The topological structures of real-world KGs, however, are rather heterogeneous, i.e., a KG is composed of multiple distinct hierarchies and non-hierarchical graph structures. Therefore, a homogeneous (either Euclidean or hyperbolic) geometry is not sufficient for fairly representing such heterogeneous structures. To capture the topological heterogeneity of KGs, we present an ultrahyperbolic KG embedding (UltraE) in an ultrahyperbolic (or pseudo-Riemannian) manifold that seamlessly interleaves hyperbolic and spherical manifolds. In particular, we model each relation as a pseudo-orthogonal transformation that preserves the pseudo-Riemannian bilinear form. The pseudo-orthogonal transformation is decomposed into various operators (i.e., circular rotations, reflections and hyperbolic rotations), allowing for simultaneously modeling heterogeneous structures as well as complex relational patterns. Experimental results on three standard KGs show that UltraE outperforms previous Euclidean, hyperbolic, and mixed-curvature KG embedding approaches.}, author = {Xiong, Bo and Zhu, Shichao and Nayyeri, Mojtaba and Xu, Chengjin and Pan, Shirui and Zhou, Chuan and Staab, Steffen}, booktitle = {28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-22}, doi = {https://doi.org/10.1145/3534678.3539333}, month = {08}, title = {Ultrahyperbolic Knowledge Graph Embeddings}, url = {https://arxiv.org/abs/2206.00449}, year = 2022 }
4. Elshani, D., Lombardi, A., Fisher, A., Staab, S., Hernández, D., & Wortmann, T. (2022, May). Knowledge Graphs for Multidisciplinary Co-Design: Introducing RDF to BHoM. In Proceedings of LDAC2022 - 10th Linked Data in Architecture and Construction Workshop. LDAC2022 - 10th Linked Data in Architecture and Construction Workshop, Hersonissos, Greece.
  - BibTeX
  BibTeX
  @inproceedings{elshaniknowledge, author = {Elshani, Diellza and Lombardi, Alessio and Fisher, Al and Staab, Steffen and Hernández, Daniel and Wortmann, Thomas}, booktitle = {In Proceedings of LDAC2022 - 10th Linked Data in Architecture and Construction Workshop}, eventdate = {May 29, 2022}, eventtitle = {LDAC2022 - 10th Linked Data in Architecture and Construction Workshop}, month = {05}, preprinturl = {https://www.researchgate.net/profile/Diellza-Elshani/publication/360962332_Knowledge_Graphs_for_Multidisciplinary_Co-Design_Introducing_RDF_to_BHoM/links/6295a34f1117461e03ace1e3/Knowledge-Graphs-for-Multidisciplinary-Co-Design-Introducing-RDF-to-BHoM.pdf}, title = {Knowledge Graphs for Multidisciplinary Co-Design: Introducing RDF to BHoM}, venue = {Hersonissos, Greece}, year = 2022 }
5. Elshani, D., Wortmann, T., & Staab, S. (2022, May). Towards Better Co-Design with Disciplinary Ontologies: Review and Evaluation of Data Interoperability in the AEC Industry. In Proceedings of LDAC2022 - 10th Linked Data in Architecture and Construction Workshop. LDAC2022 - 10th Linked Data in Architecture and Construction Workshop, Hersonissos, Greece.
  - BibTeX
  BibTeX
  @conference{elshani2022towards, author = {Elshani, Diellza and Wortmann, Thomas and Staab, Steffen}, booktitle = {In Proceedings of LDAC2022 - 10th Linked Data in Architecture and Construction Workshop}, eventdate = {May 29, 2022}, eventtitle = {LDAC2022 - 10th Linked Data in Architecture and Construction Workshop}, month = {05}, preprinturl = {https://www.researchgate.net/publication/360962321_Towards_Better_Co-Design_with_Disciplinary_Ontologies_Review_and_Evaluation_of_Data_Interoperability_in_the_AEC_Industry}, title = {Towards Better Co-Design with Disciplinary Ontologies: Review and Evaluation of Data Interoperability in the AEC Industry.}, venue = {Hersonissos, Greece}, year = 2022 }
6. Shoukry, N., Abd El Ghany, M. A., & Salem, M. A.-M. (2022). Multi-Modal Long-Term Person Re-Identification Using Physical Soft Bio-Metrics and Body Figure. Applied Sciences, 12(6), Article 6. https://doi.org/10.3390/app12062835
  - BibTeX
  BibTeX
  @article{Shoukry_2022, author = {Shoukry, Nadeen and Abd El Ghany, Mohamed A. and Salem, Mohammed A.-M.}, doi = {10.3390/app12062835}, issn = {2076-3417}, journal = {Applied Sciences}, month = {03}, number = 6, pages = 2835, publisher = {MDPI AG}, title = {Multi-Modal Long-Term Person Re-Identification Using Physical Soft Bio-Metrics and Body Figure}, url = {http://dx.doi.org/10.3390/app12062835}, volume = 12, year = 2022 }
7. Alayary, Y., Shoukry, N., Ghany, M. A. A. E., & Salem, M. A.-M. (2022). Face Masked and Unmasked Humans Detection and Tracking in Video Surveillance. 4th Novel Intelligent and Leading Emerging Sciences Conference, NILES 2022, Giza, Egypt, October 22-24, 2022, 211--215. https://doi.org/10.1109/NILES56402.2022.9942375
  - BibTeX
  BibTeX
  @inproceedings{DBLP:conf/niles/AlayarySGS22, author = {Alayary, Yomna and Shoukry, Nadeen and Ghany, Mohamed A. Abd El and Salem, Mohammed A.{-}M.}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {4th Novel Intelligent and Leading Emerging Sciences Conference, {NILES} 2022, Giza, Egypt, October 22-24, 2022}, doi = {10.1109/NILES56402.2022.9942375}, pages = {211--215}, publisher = {{IEEE}}, title = {Face Masked and Unmasked Humans Detection and Tracking in Video Surveillance}, url = {https://doi.org/10.1109/NILES56402.2022.9942375}, year = 2022 }
8. Baier, A., & Staab, S. (2022). A Simulated 4-DOF Ship Motion Dataset for System Identification under Environmental Disturbances. https://doi.org/10.18419/darus-2905
  - BibTeX
  BibTeX
  @dataset{baier2022simulated, abstract = {This dataset contains data of 125 1-hour simulations of ship motion during various sea states performing random maneuvers in 4 degrees of freedom (surge-sway-yaw-roll). The original ship is a patrol ship developed by Perez et al. [1]. We have extended it with a set of two symmetrically placed rudder propellers. Additionally, we simulate wind forces according to Isherwood's wind model [2]. Wind-induced waves are generated with the JONSWAP spectrum [3] and the corresponding wave forces are then computed using wave force response amplitude operators (ROA).Implementations of the ship model, Isherwood's wave model, wave force ROAs and the JONSWAP spectrum can be found in the Marine Systems Simulator toolbox by Fossen and Perez [4].The dataset is split into a routine operation set (96 hours) and into an Out-Of-Distribution (OOD) set (29 hours). The routine operation set is split into train-validation-test with a 60-10-30 split, while the OOD set is used solely for testing.The dataset is used for the evaluation of nonlinear system identification methods for multi-step predictions. The following inputs and outputs are considered for the identification problem. Inputs are the shaft speeds of both propellers, their azimut angles, wind angle of attack, and wind speed. Measured states or outputs are surge velocity, sway velocity and roll rate, as well as yaw angle and roll angle.Please see the README.txt file for details regarding the file structure of this dataset and a description of the variables in the .tab files.This research is funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 - 390740016. We acknowledge the support by the Stuttgart Center for Simulation Science (SimTech).[1] T. Perez, A. Ross, and T. I. Fossen, “A 4-DOF SIMULINK model of acoastal patrol vessel for manoeuvring in waves,” in IFAC MCMC, 2006.[2] R. M. Isherwood, “Wind resistance of merchant ships,” The RoyalInstitution of Naval Architects, 1972.[3] K. Hasselmann, T. Barnett, E. Bouws, H. Carlson, D. Cartwright, K. Enke,J. Ewing, H. Gienapp, D. Hasselmann, P. Kruseman, A. Meerburg,P. Muller, D. Olbers, K. Richter, W. Sell, and H. Walden, “Measurementsof wind-wave growth and swell decay during the joint north sea waveproject (JONSWAP),” Deut. Hydrogr. Z., vol. 8, pp. 1-95, 01 1973.[4] T. I. Fossen and T. Perez, “Marine Systems Simulator (MSS),” https://github.com/cybergalactic/MSS, 2004, last accessed: 2022-06-14.}, affiliation = {Baier, Alexandra/Universität Stuttgart, Staab, Steffen/Universität Stuttgart}, author = {Baier, Alexandra and Staab, Steffen}, doi = {10.18419/darus-2905}, howpublished = {Dataset}, note = {Related to: Baier, A., Boukhers, Z., & Staab, S. (2021). Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction. arXiv: 2103.06727}, orcid-numbers = {Baier, Alexandra/0000-0001-5609-3400, Staab, Steffen/0000-0002-0780-4154}, title = {A Simulated 4-DOF Ship Motion Dataset for System Identification under Environmental Disturbances}, year = 2022 }
9. Dragoi, M., Burceanu, E., Haller, E., Manolache, A., & Brad, F. (2022). AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection. Thirty-Sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track. https://openreview.net/forum?id=rbrouCKPiej
  - BibTeX
  BibTeX
  @inproceedings{dragoi2022anoshift, author = {Dragoi, Marius and Burceanu, Elena and Haller, Emanuela and Manolache, Andrei and Brad, Florin}, booktitle = {Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, title = {AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection}, url = {https://openreview.net/forum?id=rbrouCKPiej}, year = 2022 }
10. Frank, D., Latif, D. A., Muehlebach, M., Unger, B., & Staab, S. (2022). Robust Recurrent Neural Network to Identify Ship Motion in Open Water with Performance Guarantees - Technical Report. https://doi.org/10.48550/arXiv.2212.05781
  - BibTeX
  BibTeX
  @misc{frank2022robust, abstract = {Recurrent neural networks are capable of learning the dynamics of an unknown nonlinear system purely from input-output measurements. However, the resulting models do not provide any stability guarantees on the input-output mapping. In this work, we represent a recurrent neural network as a linear time-invariant system with nonlinear disturbances. By introducing constraints on the parameters, we can guarantee finite gain stability and incremental finite gain stability. We apply this identification method to learn the motion of a four-degrees-of-freedom ship that is moving in open water and compare it against other purely learning-based approaches with unconstrained parameters. Our analysis shows that the constrained recurrent neural network has a lower prediction accuracy on the test set, but it achieves comparable results on an out-of-distribution set and respects stability conditions.}, archiveprefix = {arXiv}, author = {Frank, Daniel and Latif, Decky Aspandi and Muehlebach, Michael and Unger, Benjamin and Staab, Steffen}, doi = {https://doi.org/10.48550/arXiv.2212.05781}, eprint = {2212.05781}, primaryclass = {cs.LG}, title = {Robust Recurrent Neural Network to Identify Ship Motion in Open Water with Performance Guarantees - Technical Report}, year = 2022 }
11. Iurshina, A., Pan, J., Boutalbi, R., & Staab, S. (2022). NILK : Entity Linking Dataset Targeting NIL-Linking Cases. In M. Al Hasan & L. Xiong (Eds.), CIKM ’22 : Proceedings of the 31st ACM International Conference on Information & Knowledge Management (pp. 4069–4073). Association for Computing Machinery. https://doi.org/10.1145/3511808.3557659
  - BibTeX
  BibTeX
  @inproceedings{iurshina2022entity, address = {New York}, affiliation = {Iurshina, A (Corresponding Author), Univ Stuttgart, Stuttgart, Germany. Iurshina, Anastasiia; Pan, Jiaxin; Boutalbi, Rafika; Staab, Steffen, Univ Stuttgart, Stuttgart, Germany.}, author = {Iurshina, Anastasiia and Pan, Jiaxin and Boutalbi, Rafika and Staab, Steffen}, booktitle = {CIKM '22 : Proceedings of the 31st ACM International Conference on Information & Knowledge Management}, doi = {10.1145/3511808.3557659}, editor = {Al Hasan, Mohammad and Xiong, Li}, eventdate = {2022-10-17/2022-10-21}, eventtitle = {CIKM '22 : Proceedings of the 31st ACM International Conference on Information & Knowledge Management}, isbn = {978-1-4503-9236-5}, language = {eng}, pages = {4069-4073}, publisher = {Association for Computing Machinery}, title = {NILK : Entity Linking Dataset Targeting NIL-Linking Cases}, unique-id = {WOS:001074639604020}, venue = {Atlanta, USA}, year = 2022 }
12. Liuzniak, A. (2022). How users attend to online comments: an eye-tracking approach [Master’s Thesis]. https://doi.org/10.18419/OPUS-11979
  - BibTeX
  BibTeX
  @mastersthesis{https://doi.org/10.18419/opus-11979, abstract = {The modern world is highly influenced by user-contributed content such as user comments, which is one of the most popular forms of communication on social media. They help build a connection between content creators and content consumers, as well as a connection between users of a social platform, which makes them highly relevant for community interaction. However, researchers have not treated users' comment reading strategies in much detail. The existing accounts are limited to address solely an explicit comment reading approach, which is only able to track an active interaction, e.g. analyze attention by counting a number of clicks. This technique can not fully estimate the drawn attention, as 73% of people do not actively interact with comments. Eye-tracking plays a vital role in solving the task of analysing implicit attention, as it allows to estimate such crucial characteristics, as comment features, which drew the most attention, the amount of attention given or the order of seeing specific comments. The current research uses eye-tracking based attention analysis for investigating the phenomena of users' reading behaviour on the real YouTube interface. The present master thesis concentrates on analysing users' attention mechanisms and reading behaviour and finding a correlation between the comment features such as length, language, sparseness, comment position, number of likes, presence of replies, presence of video creator's like and authorised user label, which cannot be exhibited from a pure textual point of view. This work shows that number of likes and presence of answers contribute the most to the attention drawn by comments. The analysis revealed that the category of a video deeply influences the emotion and length of the popular comments: if people are looking for useful information, which applies to educational videos, they tend to pay attention to neutral and long comments, whereas while looking for entertainment, people most probably will notice short and positive comments. The two important findings from the gender analysis are that women tend to read longer comments, but skip a higher percentage of comments than men. It is hoped that this research will contribute to a deeper understanding of the features that draw the most attention, which can be exploited in content generation strategies and developing new ranking algorithms.}, author = {Liuzniak, Alona}, copyright = {info:eu-repo/semantics/openAccess}, doi = {10.18419/OPUS-11979}, institution = {Institute for Parallel and Distributed Systems}, language = {en}, school = {Department of Analytical Computing, Institute for Parallel and Distributed Systems, Universität Stuttgart, }, title = {How users attend to online comments: an eye-tracking approach}, type = {Master's Thesis}, url = {http://elib.uni-stuttgart.de/handle/11682/11996}, year = 2022 }
13. Manolache, A., Brad, F., Barbalau, A., Ionescu, R. T., & Popescu, M. (2022). VeriDark: A Large-Scale Benchmark for Authorship Verification on the Dark Web. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), Advances in Neural Information Processing Systems (Vol. 35, pp. 15574--15588). Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2022/file/64008fa30cba9b4d1ab1bd3bd3d57d61-Paper-Datasets_and_Benchmarks.pdf
  - BibTeX
  BibTeX
  @inproceedings{NEURIPS2022_64008fa3, author = {Manolache, Andrei and Brad, Florin and Barbalau, Antonio and Ionescu, Radu Tudor and Popescu, Marius}, booktitle = {Advances in Neural Information Processing Systems}, editor = {Koyejo, S. and Mohamed, S. and Agarwal, A. and Belgrave, D. and Cho, K. and Oh, A.}, pages = {15574--15588}, publisher = {Curran Associates, Inc.}, title = {VeriDark: A Large-Scale Benchmark for Authorship Verification on the Dark Web}, url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/64008fa30cba9b4d1ab1bd3bd3d57d61-Paper-Datasets_and_Benchmarks.pdf}, volume = 35, year = 2022 }
14. Potyka, N., Bazo, M., Spieler, J., & Staab, S. (2022). Learning Gradual Argumentation Frameworks using Meta-heuristics. In I. Kuhlmann, J. Mumford, & S. Sarkadi (Eds.), Proceedings of the 1st Workshop on Argumentation & Machine Learning co-located with 9th International Conference on Computational Models of Argument (COMMA 2022), Cardiff, Wales, September 13th, 2022 (Vol. 3208, pp. 96–108). CEUR-WS.org. https://ceur-ws.org/Vol-3208/paper7.pdf
  - BibTeX
  BibTeX
  @inproceedings{DBLP:conf/comma/PotykaBSS22, author = {Potyka, Nico and Bazo, Mohamad and Spieler, Jonathan and Staab, Steffen}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {Proceedings of the 1st Workshop on Argumentation & Machine Learning co-located with 9th International Conference on Computational Models of Argument (COMMA 2022), Cardiff, Wales, September 13th, 2022}, editor = {Kuhlmann, Isabelle and Mumford, Jack and Sarkadi, Stefan}, pages = {96-108}, publisher = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, title = {Learning Gradual Argumentation Frameworks using Meta-heuristics}, url = {https://ceur-ws.org/Vol-3208/paper7.pdf}, volume = 3208, year = 2022 }
15. Schneider, T., Qiu, C., Kloft, M., Aspandi-Latif, D., Staab, S., Mandt, S., & Rudolph, M. (2022). Detecting Anomalies within Time Series using Local Neural Transformations. ArXiv Preprint. https://arxiv.org/abs/2202.03944
  - BibTeX
  BibTeX
  @article{schneider2022detecting, author = {Schneider, Tim and Qiu, Chen and Kloft, Marius and Aspandi-Latif, Decky and Staab, Steffen and Mandt, Stephan and Rudolph, Maja}, ee = {https://arxiv.org/abs/2202.03944}, journal = {arXiv preprint}, title = {Detecting Anomalies within Time Series using Local Neural Transformations.}, url = {https://arxiv.org/abs/2202.03944}, year = 2022 }
16. Sengupta, K., N., F., & Staab, S. (2022). Accessibility of Online Educational Platforms. MPDAS Workshop (Multidisciplinary Perspectives on Designing Accessible Systems for Users with Multiple Impairments: Grand Challenges and Opportunities for Future Research Workshop, The 24th International ACM SIGACCESS Conference on Computers and Accessibility), 23. October 2022.
  - BibTeX
  BibTeX
  @article{sengupta2022accessibility, author = {Sengupta, K. and N., Fathallah and Staab, S}, booktitle = {MPDAS Workshop (Multidisciplinary Perspectives on Designing Accessible Systems for Users with Multiple Impairments: Grand Challenges and Opportunities for Future Research workshop, The 24th International ACM SIGACCESS Conference on Computers and Accessibility), 23. October 2022.}, language = {en}, title = {Accessibility of Online Educational Platforms}, year = 2022 }
17. Xiong, B., Cochez, M., Nayyeri, M., & Staab, S. (2022). Hyperbolic Embedding Inference for Structured Multi-Label Prediction. Advances in Neural Information Processing Systems 2022. https://openreview.net/forum?id=XFnDhcEH9FF
  - BibTeX
  BibTeX
  @conference{noauthororeditor, author = {Xiong, Bo and Cochez, Michael and Nayyeri, Mojtaba and Staab, Steffen}, booktitle = {Advances in Neural Information Processing Systems 2022}, title = {Hyperbolic Embedding Inference for Structured Multi-Label Prediction}, url = {https://openreview.net/forum?id=XFnDhcEH9FF}, year = 2022 }
18. Xiong, B., Potyka, N., Tran, T.-K., Nayyeri, M., & Staab, S. (2022). Faithful Embedding for EL++ Knowledge Bases. Proceedings of the 21st International Semantic Web Conference (ISWC 2022), 1–16. https://arxiv.org/abs/2201.09919
  - BibTeX
  BibTeX
  @inproceedings{noauthororeditor2022faithful, author = {Xiong, Bo and Potyka, Nico and Tran, T.-K. and Nayyeri, Mojtaba and Staab, Steffen}, booktitle = {Proceedings of the 21st International Semantic Web Conference (ISWC 2022)}, pages = {1-16}, title = {Faithful Embedding for EL++ Knowledge Bases}, url = {https://arxiv.org/abs/2201.09919}, year = 2022 }
19. Xiong, B., Zhu, S., Potyka, N., Pan, S., Zhou, C., & Staab, S. (2022). Pseudo-Riemannian Graph Convolutional Networks. Advances in Neural Information Processing Systems. https://arxiv.org/abs/2106.03134
  - BibTeX
  BibTeX
  @inproceedings{xiong2022pseudoriemannian, abstract = {Graph Convolutional Networks (GCNs) are typically studied through the lens of Euclidean geometry. Non-Euclidean Riemannian manifolds provide specific inductive biases for embedding hierarchical or spherical data, but cannot align well with data of mixed topologies. We consider a larger class of semi-Riemannian manifolds with indefinite metric that generalize hyperboloid and sphere as well as their submanifolds. We develop new geodesic tools that allow for extending neural network operations into geodesically disconnected semi-Riemannian manifolds. As a consequence, we derive a principled Semi-Riemannian GCN that first models data in semi-Riemannian manifolds of constant nonzero curvature in the context of graph neural networks. Our method provides a geometric inductive bias that is sufficiently flexible to model mixed heterogeneous topologies like hierarchical graphs with cycles. Empirical results demonstrate that our method outperforms Riemannian counterparts when embedding graphs of complex topologies.}, author = {Xiong, Bo and Zhu, Shichao and Potyka, Nico and Pan, Shirui and Zhou, Chuan and Staab, Steffen}, booktitle = {Advances in Neural Information Processing Systems}, title = {Pseudo-Riemannian Graph Convolutional Networks}, url = {https://arxiv.org/abs/2106.03134}, year = 2022 }
2021
1. Fathallah, N., Ehab, F., & Salem, M. A.-M. (2021). An Improved Deep Learning Model for Early Fire and Smoke Detection on Edge Vision Unit. 2021 Tenth International Conference on Intelligent Computing and Information Systems (ICICIS), 66–73. https://doi.org/10.1109/ICICIS52592.2021.9694193
  - BibTeX
  BibTeX
  @inproceedings{9694193, abstract = {Despite the large surges in technology investment and the dominance of smart systems, some emergency events remain unsolved or solved with a massive trade-off between performance and portability. The sudden fire outbreak that may occur due to several reasons is one of the problems that is yet to be solved, a natural disaster that could cause massive ecological and economic losses. Therefore, it is crucial to develop precise, fast, and commercially available solutions. Several image classification approaches were effectively designed, However, most of these models contain many layers that often lead to a massive model size such as AlexNet and GoogleNet. In this paper, we propose a model inspired by FireNet that balances and softens the tradeoff between performance and model size. In this work, smoke detection is augmented to FireNet, the model is trained and tested on various datasets and has an accuracy of 83% which is quite promising as compared to other CNN models that are deployed on an embedded device such as raspberry pi.}, author = {Fathallah, Nadeen and Ehab, Farah and Salem, Mohammed A.-M}, booktitle = {2021 Tenth International Conference on Intelligent Computing and Information Systems (ICICIS)}, doi = {10.1109/ICICIS52592.2021.9694193}, month = {12}, pages = {66-73}, title = {An Improved Deep Learning Model for Early Fire and Smoke Detection on Edge Vision Unit}, year = 2021 }
2. Gamal, A., Shoukry, N., & Salem, M. A.-M. (2021). Long-Term Person Re-identification Model with a Strong Feature Extractor. 2021 Tenth International Conference on Intelligent Computing and Information Systems (ICICIS), 74–79. https://doi.org/10.1109/ICICIS52592.2021.9694212
  - BibTeX
  BibTeX
  @inproceedings{9694212, abstract = {Person Re-Identification is a challenging task in the field of computer vision, it has gained a huge space due to its importance in our life. Person Re-ID is mainly utilized in security applications, it is used everywhere like airports, metro stations, banks, etc. Due to its crucial importance many approaches were proposed to perform such task, the explosion of deep learning and its ability to outperform most of the state of-the-art approaches motivated researchers to use it in person re-identification, moreover researchers introduced benchmark person re-id datasets to train these models, which were able to provide remarkable results and provide a solid benchmark for comparison. The only limitation to all these models and datasets is that they were built assuming no pedestrian clothes variations. In our work we aim to address the problem of Long Term Person Re-Identification where the clothes variation exists. We start by experimenting and comparing the most recent state-of-the-art deep learning models that were developed for that task such as OSNet and PCB. After that we introduce our proposal to replace the backbone network of PCB with OSNet to act as a feature extractor. All of these models were trained and tested using NKUP dataset and Market-1501. The benchmark dataset Market-1501 was used to compare our model’s performance with several standard models. Our model was able to outperform the" PCB" model on Market-1501 and improve the accuracy from 87.71% to 91.3%. On the NKUP dataset our model improved the accuracy of" PCB" model from 16.1% to 17.6%.}, author = {Gamal, Adham and Shoukry, Nadeen and Salem, Mohammed A.-M.}, booktitle = {2021 Tenth International Conference on Intelligent Computing and Information Systems (ICICIS)}, doi = {10.1109/ICICIS52592.2021.9694212}, month = {12}, pages = {74-79}, title = {Long-Term Person Re-identification Model with a Strong Feature Extractor}, year = 2021 }
3. Hernández, D., Galárraga, L., & Hose, K. (2021). Computing how-provenance for SPARQL queries via query rewriting (No. 13). 14(13), Article 13. https://doi.org/10.14778/3484224.3484235
  - BibTeX
  BibTeX
  @article{2021, author = {Hernández, Daniel and Galárraga, Luis and Hose, Katja}, doi = {10.14778/3484224.3484235}, month = {09}, number = 13, pages = {3389--3401}, publisher = {{VLDB} Endowment}, title = {Computing how-provenance for SPARQL queries via query rewriting}, url = {https://doi.org/10.14778%2F3484224.3484235}, volume = 14, year = 2021 }
4. Manolache, A., Brad, F., & Burceanu, E. (2021). DATE: Detecting Anomalies in Text via Self-Supervision of Transformers. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 267--277. https://doi.org/10.18653/v1/2021.naacl-main.25
  - BibTeX
  BibTeX
  @inproceedings{manolache-etal-2021-date, abstract = {Leveraging deep learning models for Anomaly Detection (AD) has seen widespread use in recent years due to superior performances over traditional methods. Recent deep methods for anomalies in images learn better features of normality in an end-to-end self-supervised setting. These methods train a model to discriminate between different transformations applied to visual data and then use the output to compute an anomaly score. We use this approach for AD in text, by introducing a novel pretext task on text sequences. We learn our DATE model end-to-end, enforcing two independent and complementary self-supervision signals, one at the token-level and one at the sequence-level. Under this new task formulation, we show strong quantitative and qualitative results on the 20Newsgroups and AG News datasets. In the semi-supervised setting, we outperform state-of-the-art results by +13.5{\%} and +6.9{\%}, respectively (AUROC). In the unsupervised configuration, DATE surpasses all other methods even when 10{\%} of its training data is contaminated with outliers (compared with 0{\%} for the others).}, address = {Online}, author = {Manolache, Andrei and Brad, Florin and Burceanu, Elena}, booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}, doi = {10.18653/v1/2021.naacl-main.25}, month = {06}, pages = {267--277}, publisher = {Association for Computational Linguistics}, title = {DATE: Detecting Anomalies in Text via Self-Supervision of Transformers}, url = {https://aclanthology.org/2021.naacl-main.25}, year = 2021 }
5. Baier, A., Boukhers, Z., & Staab, S. (2021). Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction. http://arxiv.org/abs/2103.06727
  - BibTeX
  BibTeX
  @preprint{baier2021hybrid, abstract = {Physical motion models offer interpretable predictions for the motion of vehicles. However, some model parameters, such as those related to aero- and hydrodynamics, are expensive to measure and are often only roughly approximated reducing prediction accuracy. Recurrent neural networks achieve high prediction accuracy at low cost, as they can use cheap measurements collected during routine operation of the vehicle, but their results are hard to interpret. To precisely predict vehicle states without expensive measurements of physical parameters, we propose a hybrid approach combining deep learning and physical motion models including a novel two-phase training procedure. We achieve interpretability by restricting the output range of the deep neural network as part of the hybrid model, which limits the uncertainty introduced by the neural network to a known quantity. We have evaluated our approach for the use case of ship and quadcopter motion. The results show that our hybrid model can improve model interpretability with no decrease in accuracy compared to existing deep learning approaches.}, author = {Baier, Alexandra and Boukhers, Zeyd and Staab, Steffen}, language = {English}, note = {cite arxiv:2103.06727}, title = {Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction}, url = {http://arxiv.org/abs/2103.06727}, year = 2021 }
6. Bazo, M. W. (2021). Learning quantitative argumentation frameworks using sparse neural networks and swarm intelligence algorithms. Department of Analytical Computing. https://doi.org/10.18419/OPUS-11903
  - BibTeX
  BibTeX
  @misc{https://doi.org/10.18419/opus-11903, abstract = {Argumentation Frameworks are an approach of formalizing arguments and their interrelations in a graph structure. They can be used to draw conclusions from this modelling of knowledge. Since argumentation is an important part of human reasoning, these graph structures can be considered easily interpretable, what makes this technology an interesting explainable artificial intelligence method. Although this is not their main purpose, Quantitative Argumentation Frameworks can be used to solve classification problems by following a new approach. This approach is based on constructing them out of multilayer perceptrons (MLP), based on the work of Potyka. In this thesis we were motivated to construct Quantitative Argumentation Frameworks out of sparse MLPs. A swarm intelligence algorithm, namely Particle Swarm Optimization (PSO), was developed to search for sparse MLP models with specific characteristics that relate to performance and topology of the graphical structures. Models were implemented, tested, and evaluated on three different datasets. The implementation includes preprocessing of the datasets, parameter learning of MLPs based on backpropagation, and structure learning of the MLP graphical structures. The evaluation involves constructing fully connected MLPs and decision trees for comparison purposes. The resulting models achieved high performance and low complexity in their structure. The PSO algorithm also proved its efficiency in solving the structure learning.}, author = {Bazo, Mohamad Wahed}, copyright = {info:eu-repo/semantics/openAccess}, doi = {10.18419/OPUS-11903}, institution = {Institute for Artificial Intelligence}, language = {en}, publisher = {Department of Analytical Computing}, school = {Universität Stuttgart}, title = {Learning quantitative argumentation frameworks using sparse neural networks and swarm intelligence algorithms}, type = {Bachelor's Thesis}, url = {http://elib.uni-stuttgart.de/handle/11682/11920}, year = 2021 }
7. Bolz, S. (2021). Multistep prediction of vehicle states using transformers. https://doi.org/10.18419/OPUS-11901
  - BibTeX
  BibTeX
  @misc{https://doi.org/10.18419/opus-11901, abstract = {Multistep prediction is the prediction of states based on initial states and a series of control inputs. This paper focuses on developing transformer models for multistep prediction of vehicle states and testing different modifications of the transformer architecture using the example of the prediction of a ship simulation. Research in NLP promises advantages with regard to training time and prediction accuracy for the transformer architecture compared to a state-of-the-art LSTM model. The author also investigates whether positional encodings are useful in this scenario and if a transformer model can learn the order of the inputs without positional encodings.}, author = {Bolz, Stefan}, copyright = {info:eu-repo/semantics/openAccess}, doi = {10.18419/OPUS-11901}, institution = {Department of Analytical Computing, Institute for Parallel and Distributed Systems}, language = {en}, school = {Universität Stuttgart}, title = {Multistep prediction of vehicle states using transformers}, type = {Bachelor's Thesis}, url = {http://elib.uni-stuttgart.de/handle/11682/11918}, year = 2021 }
8. Evci, H. (2021). Extracting and segmenting high-variance references from PDF documents with BERT. https://doi.org/10.18419/OPUS-11940
  - BibTeX
  BibTeX
  @misc{https://doi.org/10.18419/opus-11940, abstract = {The extraction and segmentation of references from scientific articles is a core task of modern digital libraries. Once references are extracted and segmented, the bibliographic information can be made publicly available and linked, enabling efficient literature study. However, references often vary in their structure and content. This makes the extraction and segmentation of references a challenging but valuable task. The purpose of this thesis is to investigate whether Bidirectional Encoder Representations from Transformers (BERT) is suitable for the extraction and segmentation of bibliographic references. Therefore, we follow a deep learning approach for the extraction and segmentation of references from PDF documents. We use a neural network architecture based on BERT, a deep language representation model that has significantly increased performance on many natural language processing tasks. Over the BERT output, we put a linear-chain Conditional Random Field. We experiment with different BERT models and input formats and also examine two approaches for reference extraction and segmentation. The experiments are evaluated on a challenging dataset that contains both English and German social science publications with highly varying references. Our results show that the best performing BERT models were pre-trained on similar data to the data that we used for the fine-tuning of the BERT models on the task of reference extraction and reference segmentation. Moreover, our findings show that long, context-based input sequences yield the best results. The extraction model identifies and extracts references with an average F1-score of 81.9%. References are segmented with an average F1-score of 93.6%. We show that our models compare well to one other previously published work. Our conclusion is that BERT is a suitable choice for reference extraction and reference segmentation.}, author = {Evci, Hasan}, copyright = {info:eu-repo/semantics/openAccess}, doi = {10.18419/OPUS-11940}, institution = {Institute of Parallel and Distributed Systems}, language = {en}, school = {Universität Stuttgart}, title = {Extracting and segmenting high-variance references from PDF documents with BERT}, type = {Bachelor's Thesis}, url = {http://elib.uni-stuttgart.de/handle/11682/11957}, year = 2021 }
9. Holeczek, C. (2021). Entwicklung eines neuronalen Netzwerks zur Optimierung der Datenübertragungsqualität von Kleinsatellitenplattformen [Master’s Thesis]. https://doi.org/10.18419/OPUS-11904
  - BibTeX
  BibTeX
  @mastersthesis{https://doi.org/10.18419/opus-11904, abstract = {Kleinsatelliten sind heute eine zunehmend wichtige Möglichkeit für die Forschung, Experimente und Nutzlasten in Erdumlaufbahnen zu bringen. Bei diesen Satellitensystemen besteht einerseits ein wachsender Bedarf an Datenübertragung zu einer Bodenstation, andererseits gelten in der Raumfahrt verschiedene Einschränkungen bei Aufbau und Betrieb von Funkkommunikationssystemen. Heutige Funksysteme haben meist statische Transceiver-Konfigurationen und die genutzten Übertragungsraten entsprechen der Worst-case-Betrachtung für den jeweiligen Fall. Neuerdings werden adaptive Ansätze für den Betrieb von Funkverbindungen zwischen Satellit und Bodenstation beschrieben, bei denen die Transceiver-Konfiguration, gesteuert durch einen lernenden Algorithmus, im Betrieb verändert und den jeweiligen äusseren Einflüssen angepasst wird. Die Grundlage für die verwendeten Algorithmen ist das Reinforcement Learning Model. Ziel dieser Arbeit war die Weiterentwicklung eines für die Raumfahrtfunkkommunikation entwickelten Algorithmus und der Test dieses Algorithmus' innerhalb einer Simulationsumgebung, um die Auswirkungen der Veränderungen zu bewerten. Der modifizierte Algorithmus wurde in einer Software-Simulationsumgebung betrieben, welche die digitale Signalverarbeitung von Sender und Empfänger sowie die sich verändernden Übertragungsbedingungen während eines Satellitenüberfluges abbildete. Die Modifikation wurde mit dem Originalalgorithmus hinsichtlich der erreichbaren Datenübertragung verglichen. Unter anderem wurde die Anzahl der für das Lernen verwendeten Neuronalen Netze variiert. Weiterhin wurden verschiedene Hyperparameter variiert und die Auswirkungen auf die Datenübertragung untersucht. Eine Anpassung der Hyperparameter führte dabei zu einer Übertragung von 57,8% mehr Daten als bei der Baseline-Implementierung. Sowohl die Veränderung in der Architektur als auch die Reduzierung der parallel ausgeführten Neuronalen Netze führte zu leichten Performanzeinbußen von 4,0% und 3,3%. Die Ergebnisse der hier durchgeführten Software-Simulationen lassen sich jedoch nicht auf Hardware-Simulationsumgebungen oder tatsächliche Funkverbindungen übertragen. Die Implementierung der verschiedenen Algorithmusvarianten erlaubt es, diese Varianten zukünftig auch mit Hardware-Übertragungsstrecken zu testen und später ein solches System auf einem Kleinsatelliten zu erproben.}, author = {Holeczek, Cedric}, copyright = {info:eu-repo/semantics/openAccess}, doi = {10.18419/OPUS-11904}, institution = {Institute for Artificial Intelligence}, language = {de}, school = {Universität Stuttgart}, title = {Entwicklung eines neuronalen Netzwerks zur Optimierung der Datenübertragungsqualität von Kleinsatellitenplattformen}, type = {Master's Thesis}, url = {http://elib.uni-stuttgart.de/handle/11682/11921}, year = 2021 }
10. Lauer, M. (2021). Gaze and voice driven hands free gaming. Department of Analytical Computing. https://doi.org/10.18419/OPUS-11902
  - BibTeX
  BibTeX
  @misc{https://doi.org/10.18419/opus-11902, abstract = {Playing computer games is one of the most common and enjoyable activities in the modern lifestyle. However, people with motor impairments who cannot use mouse and keyboard are not able to interact with computing devices as required by the game design. In this regard, novel interaction techniques can enable hands-free control using gaze and voice as input modalities to assist people with motor and speech impairments. This is the first evaluation of a video game control method consisting of a combination of eye tracking and non-verbal voice commands (e.g. humming) applied in a 2D jump-and-run game environment involving essential spatio-temporal interactions. To assess interaction feasibility, the evaluation study consisted of both qualitative and quantitative measures. In addition, the feasibility was validated with a target user group of people with motor impairments. Overall, the results indicate a lower but competitive performance while increasing the fun factor.}, author = {Lauer, Mike}, copyright = {info:eu-repo/semantics/openAccess}, doi = {10.18419/OPUS-11902}, institution = {Institute for Artificial Intelligence}, language = {en}, publisher = {Department of Analytical Computing}, school = {Universität Stuttgart}, title = {Gaze and voice driven hands free gaming}, type = {Bachelor's Thesis}, url = {http://elib.uni-stuttgart.de/handle/11682/11919}, year = 2021 }
11. Lukas Schmelzeisen, Corina Dima, S. S. (2021). Wikidated 1.0: An Evolving Knowledge Graph Dataset of Wikidata’s Revision History. Wikidata Workshop @ ISWC 2021, 2982.
  - BibTeX
  BibTeX
  @inproceedings{DBLP:conf/semweb/SchmelzeisenDS21, author = {{Lukas Schmelzeisen, Corina Dima}, Steffen Staab}, booktitle = {Wikidata Workshop @ ISWC 2021}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, title = {Wikidated 1.0: An Evolving Knowledge Graph Dataset of Wikidata's Revision History}, volume = 2982, year = 2021 }
12. Miolane, N., Caorsi, M., Lupo, U., Guerard, M., Guigui, N., Mathe, J., Cabanes, Y., Reise, W., Davies, T., Leitão, A., Mohapatra, S., Utpala, S., Shailja, S., Corso, G., Liu, G., Iuricich, F., Manolache, A., Nistor, M., Bejan, M., … Long, Y. (2021). ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results.
  - BibTeX
  BibTeX
  @misc{miolane2021iclr, archiveprefix = {arXiv}, author = {Miolane, Nina and Caorsi, Matteo and Lupo, Umberto and Guerard, Marius and Guigui, Nicolas and Mathe, Johan and Cabanes, Yann and Reise, Wojciech and Davies, Thomas and Leitão, António and Mohapatra, Somesh and Utpala, Saiteja and Shailja, Shailja and Corso, Gabriele and Liu, Guoxi and Iuricich, Federico and Manolache, Andrei and Nistor, Mihaela and Bejan, Matei and Nicolicioiu, Armand Mihai and Luchian, Bogdan-Alexandru and Stupariu, Mihai-Sorin and Michel, Florent and Duc, Khanh Dao and Abdulrahman, Bilal and Beketov, Maxim and Maignant, Elodie and Liu, Zhiyuan and Černý, Marek and Bauw, Martin and Velasco-Forero, Santiago and Angulo, Jesus and Long, Yanan}, eprint = {2108.09810}, primaryclass = {cs.CG}, title = {ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results}, year = 2021 }
13. Potyka, N. (2021). Interpreting Neural Networks as Quantitative Argumentation Frameworks. Proceedings of the AAAI Conference on Artificial Intelligence, 35, 7, Article 35, 7. https://ojs.aaai.org/index.php/AAAI/article/view/16801
  - BibTeX
  BibTeX
  @inproceedings{potyka2021interpreting, address = {Palo Alto}, affiliation = {Potyka, N (Corresponding Author), Univ Stuttgart, Univ Str 32, D-70569 Stuttgart, Germany. Potyka, Nico, Univ Stuttgart, Univ Str 32, D-70569 Stuttgart, Germany.}, author = {Potyka, Nico}, booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence}, eventdate = {2021-02-02/2021-02-09}, eventtitle = {35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence}, isbn = {978-1-57735-866-4}, language = {eng}, number = {35, 7}, pages = {6463-6470}, publisher = {Association for the Advancement of Artificial Intelligence}, research-areas = {Computer Science; Education & Educational Research}, title = {Interpreting Neural Networks as Quantitative Argumentation Frameworks}, unique-id = {WOS:000680423506065}, url = {https://ojs.aaai.org/index.php/AAAI/article/view/16801}, venue = {Online}, year = 2021 }
14. Potyka, N. (2021). Generalizing Complete Semantics to Bipolar Argumentation Frameworks. In J. Vejnarová & N. Wilson (Eds.), Symbolic and Quantitative Approaches to Reasoning with Uncertainty : 16th European Conference, ECSQARU 2021 Prague, Czech Republic, September 21-24, 2021, Proceedings (No. 12897; Issue 12897, pp. 130–143). Springer. https://doi.org/10.1007/978-3-030-86772-0_10
  - BibTeX
  BibTeX
  @inproceedings{potyka2021generalizing, address = {Cham}, affiliation = {Potyka, N (Corresponding Author), Univ Stuttgart, Stuttgart, Germany. Potyka, Nico, Univ Stuttgart, Stuttgart, Germany.}, author = {Potyka, Nico}, booktitle = {Symbolic and Quantitative Approaches to Reasoning with Uncertainty : 16th European Conference, ECSQARU 2021 Prague, Czech Republic, September 21-24, 2021, Proceedings}, doi = {10.1007/978-3-030-86772-0_10}, editor = {Vejnarová, Jiřina and Wilson, Nic}, eventdate = {2021-09-21/2021-09-24}, eventtitle = {16th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2021)}, isbn = {{978-3-030-86771-3} and {978-3-030-86772-0}}, language = {eng}, number = 12897, pages = {130-143}, publisher = {Springer}, research-areas = {Computer Science; Mathematics; Science & Technology - Other Topics}, series = {Lecture Notes in Computer Science}, title = {Generalizing Complete Semantics to Bipolar Argumentation Frameworks}, unique-id = {WOS:000711926000010}, venue = {Prague, Czech Republic}, year = 2021 }
15. Schmelzeisen, L., Dima, C., & Staab, S. (2021). Wikidated 1.0: An Evolving Knowledge Graph Dataset of Wikidata’s Revision History. Wikidata Workshop @ ISWC 2021, 2982.
  - BibTeX
  BibTeX
  @inproceedings{DBLP:conf/semweb/SchmelzeisenDS21, author = {Schmelzeisen, Lukas and Dima, Corina and Staab, Steffen}, booktitle = {Wikidata Workshop @ ISWC 2021}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, title = {Wikidated 1.0: An Evolving Knowledge Graph Dataset of Wikidata's Revision History}, volume = 2982, year = 2021 }
16. Yuan, Y., Pan, J., Jia, X., Liu, L., & Peng, M. (2021). DCEN: A Decoupled Context Enhanced Network For Few-shot Slot Tagging. 2021 International Joint Conference on Neural Networks (IJCNN), 1–7. https://doi.org/10.1109/IJCNN52387.2021.9533361
  - BibTeX
  BibTeX
  @inproceedings{9533361, author = {Yuan, Youliang and Pan, Jiaxin and Jia, Xu and Liu, Luchen and Peng, Min}, booktitle = {2021 International Joint Conference on Neural Networks (IJCNN)}, doi = {10.1109/IJCNN52387.2021.9533361}, pages = {1-7}, title = {DCEN: A Decoupled Context Enhanced Network For Few-shot Slot Tagging}, year = 2021 }
2020
1. Kyza, E. A., Varda, C., Panos, D., Karageorgiou, M., Komendantova-Amann, N., Coppolino Perfumi, S., Shah, S. I. H., & Hosseini, A. S. (2020). Combating misinformation online: re-imagining social media for policy-making. Internet Policy Review. https://www.econstor.eu/handle/10419/225651
  - BibTeX
  BibTeX
  @article{noauthororeditor2020combating, abstract = {Social media have created communication channels between citizens and policymakers but are also susceptible to rampant misinformation. This new context demands new social media policies that can aid policymakers in making evidence-based decisions for combating misinformation online. This paper reports on data collected from policymakers in Austria, Greece, and Sweden, using focus groups and in-depth interviews. Analyses provide insights into challenges and identify four important themes for supporting policy-making for combating misinformation: a) creating a trusted network of experts and collaborators, b) facilitating the validation of online information, c) providing access to visualisations of data at different levels of granularity, and d) increasing the transparency and explainability of flagged misinformative content. These recommendations have implications for rethinking how revised social media policies can contribute to evidence-based decision-making.}, author = {Kyza, Eleni A. and Varda, Christiana and Panos, Dionysis and Karageorgiou, Melina and Komendantova-Amann, Nadejda and Coppolino Perfumi, Serena and Shah, Syed Iftikhar Husain and Hosseini, Akram Sadat}, journal = {Internet Policy Review}, title = {Combating misinformation online: re-imagining social media for policy-making}, url = {https://www.econstor.eu/handle/10419/225651}, year = 2020 }
2. Potyka, N. (2020). Abstract Argumentation with Markov Networks. European Conference on Artificial Intelligence (ECAI), 865–872. https://www.researchgate.net/publication/338886145_Abstract_Argumentation_with_Markov_Networks
  - BibTeX
  BibTeX
  @inproceedings{potyka20ecai, author = {Potyka, Nico}, booktitle = {European Conference on Artificial Intelligence (ECAI)}, pages = {865-872}, title = {Abstract Argumentation with Markov Networks}, url = {https://www.researchgate.net/publication/338886145_Abstract_Argumentation_with_Markov_Networks}, year = 2020 }
3. Potyka, N. (2020). Bipolar Abstract Argumentation with Dual Attacks and Supports. In D. Calvanese, E. Erdem, & M. Thielscher (Eds.), Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning (pp. 677–686). IJCAI Organization. https://doi.org/10.24963/kr.2020/69
  - BibTeX
  BibTeX
  @inproceedings{potyka2020bipolar, affiliation = {Potyka, N (Corresponding Author), Univ Stuttgart, Stuttgart, Germany. Potyka, Nico, Univ Stuttgart, Stuttgart, Germany.}, author = {Potyka, Nico}, booktitle = {Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning}, doi = {10.24963/kr.2020/69}, editor = {Calvanese, Diego and Erdem, Esra and Thielscher, Michael}, eventdate = {2020-09-12/2020-09-18}, eventtitle = {17th International Conference on Principles of Knowledge Representation and Reasoning (KR and R)}, isbn = {978-0-9992411-7-2}, language = {eng}, pages = {677-686}, publisher = {IJCAI Organization}, research-areas = {Computer Science; Science & Technology - Other Topics}, title = {Bipolar Abstract Argumentation with Dual Attacks and Supports}, unique-id = {WOS:000720083100068}, venue = {Online}, year = 2020 }
4. Shoukry, N., Elkilany, O., Thiam, P., Kessler, V., & Schwenker, F. (2020). Subject-independent Pain Recognition using Physiological Signals and Para-linguistic Vocalizations. Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods, 142–150. https://doi.org/10.5220/0008912201420150
  - BibTeX
  BibTeX
  @inproceedings{Shoukry_2020, author = {Shoukry, Nadeen and Elkilany, Omar and Thiam, Patrick and Kessler, Viktor and Schwenker, Friedhelm}, booktitle = {Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods}, doi = {10.5220/0008912201420150}, pages = {142–150}, publisher = {SCITEPRESS - Science and Technology Publications}, title = {Subject-independent Pain Recognition using Physiological Signals and Para-linguistic Vocalizations}, url = {http://dx.doi.org/10.5220/0008912201420150}, year = 2020 }
5. Wong, E., Schneider, T., Schmitt, J., Schmidt, F. R., & Kolter, J. Z. (2020). Neural Network Virtual Sensors for Fuel Injection Quantities with Provable Performance Specifications. 2020 IEEE Intelligent Vehicles Symposium (IV), 1753–1758. https://doi.org/10.1109/IV47402.2020.9304765
  - BibTeX
  BibTeX
  @inproceedings{9304765, author = {Wong, Eric and Schneider, Tim and Schmitt, Joerg and Schmidt, Frank R. and Kolter, J. Zico}, booktitle = {2020 IEEE Intelligent Vehicles Symposium (IV)}, doi = {10.1109/IV47402.2020.9304765}, pages = {1753-1758}, title = {Neural Network Virtual Sensors for Fuel Injection Quantities with Provable Performance Specifications}, year = 2020 }
2019
1. Dima, C., de Kok, D., Witte, N., & Hinrichs, E. (2019). No Word is an Island---A Transformation Weighting Model for Semantic Composition. Transactions of the Association for Computational Linguistics, 7, 437--451. https://doi.org/10.1162/tacl_a_00275
  - BibTeX
  BibTeX
  @article{dima-etal-2019-word, abstract = {Composition models of distributional semantics are used to construct phrase representations from the representations of their words. Composition models are typically situated on two ends of a spectrum. They either have a small number of parameters but compose all phrases in the same way, or they perform word-specific compositions at the cost of a far larger number of parameters. In this paper we propose transformation weighting (TransWeight), a composition model that consistently outperforms existing models on nominal compounds, adjective-noun phrases, and adverb-adjective phrases in English, German, and Dutch. TransWeight drastically reduces the number of parameters needed compared with the best model in the literature by composing similar words in the same way.}, address = {Cambridge, MA}, author = {Dima, Corina and de Kok, Dani{\"e}l and Witte, Neele and Hinrichs, Erhard}, doi = {10.1162/tacl_a_00275}, editor = {Lee, Lillian and Johnson, Mark and Roark, Brian and Nenkova, Ani}, journal = {Transactions of the Association for Computational Linguistics}, pages = {437--451}, publisher = {MIT Press}, title = {No Word is an {I}sland{---}{A} Transformation Weighting Model for Semantic Composition}, url = {https://aclanthology.org/Q19-1025}, volume = 7, year = 2019 }
2. Dima, G.-C. (2019). Composition Models for the Representation and Semantic Interpretation of Nominal Compounds [Universität Tübingen]. https://doi.org/10.15496/PUBLIKATION-28485
  - BibTeX
  BibTeX
  @phdthesis{https://doi.org/10.15496/publikation-28485, author = {Dima, Gina-Corina}, copyright = {ubt-podok}, doi = {10.15496/PUBLIKATION-28485}, language = {en}, publisher = {Universität Tübingen}, title = {Composition Models for the Representation and Semantic Interpretation of Nominal Compounds}, url = {https://publikationen.uni-tuebingen.de/xmlui/handle/10900/87098}, year = 2019 }
2018
1. Frank, D., Zelazo, D., & Allgöwer, F. (2018). Bearing-Only Formation Control with Limited Visual Sensing: Two Agent Case. IFAC-PapersOnLine, 51(23), Article 23. https://doi.org/10.1016/j.ifacol.2018.12.006
  - BibTeX
  BibTeX
  @article{Frank_2018, author = {Frank, Daniel and Zelazo, Daniel and Allgöwer, Frank}, doi = {10.1016/j.ifacol.2018.12.006}, journal = {{IFAC}-{PapersOnLine}}, number = 23, pages = {28--33}, publisher = {Elsevier {BV}}, title = {Bearing-Only Formation Control with Limited Visual Sensing: Two Agent Case}, url = {https://doi.org/10.1016%2Fj.ifacol.2018.12.006}, volume = 51, year = 2018 }
2. Hernández, D., Gutierrez, C., & Angles, R. (2018). The problem of correlation and substitution in SPARQL-Extended Version. In arXiv (ArXiv). https://doi.org/10.48550/arXiv.1801.04387
  - BibTeX
  BibTeX
  @techreport{Herńandez2018, author = {Hernández, Daniel and Gutierrez, Claudio and Angles, Renzo}, doi = {https://doi.org/10.48550/arXiv.1801.04387}, journal = {arXiv}, title = {The problem of correlation and substitution in SPARQL-Extended Version}, url = {http://arxiv.org/abs/1801.04387}, year = 2018 }
3. Hernández, D., Gutiérrez, C., & Hogan, A. (2018). Certain Answers for SPARQL with Blank Nodes. In D. Vrandecic, K. Bontcheva, M. C. Suárez-Figueroa, V. Presutti, I. Celino, M. Sabou, L.-A. Kaffee, & E. Simperl (Eds.), The Semantic Web - ISWC 2018 - 17th International Semantic Web Conference, Monterey, CA, USA, October 8-12, 2018, Proceedings, Part I (Vol. 11136, pp. 337--353). Springer. https://doi.org/10.1007/978-3-030-00671-6_20
  - BibTeX
  BibTeX
  @inproceedings{DBLP:conf/semweb/HernandezGH18, author = {Hernández, Daniel and Gutiérrez, Claudio and Hogan, Aidan}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {The Semantic Web - ISWC 2018 - 17th International Semantic Web Conference, Monterey, CA, USA, October 8-12, 2018, Proceedings, Part {I}}, doi = {10.1007/978-3-030-00671-6_20}, editor = {Vrandecic, Denny and Bontcheva, Kalina and Suárez-Figueroa, Mari Carmen and Presutti, Valentina and Celino, Irene and Sabou, Marta and Kaffee, Lucie{-}Aim{\'{e}}e and Simperl, Elena}, pages = {337--353}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Certain Answers for SPARQL with Blank Nodes}, url = {https://doi.org/10.1007/978-3-030-00671-6_20}, volume = 11136, year = 2018 }
2017
1. de Kok, D., Ma, J., Dima, C., & Hinrichs, E. (2017). PP Attachment: Where do We Stand? In M. Lapata, P. Blunsom, & A. Koller (Eds.), Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers (pp. 311--317). Association for Computational Linguistics. https://aclanthology.org/E17-2050
  - BibTeX
  BibTeX
  @inproceedings{de-kok-etal-2017-pp, abstract = {Prepostitional phrase (PP) attachment is a well known challenge to parsing. In this paper, we combine the insights of different works, namely: (1) treating PP attachment as a classification task with an arbitrary number of attachment candidates; (2) using auxiliary distributions to augment the data beyond the hand-annotated training set; (3) using topological fields to get information about the distribution of PP attachment throughout clauses and (4) using state-of-the-art techniques such as word embeddings and neural networks. We show that jointly using these techniques leads to substantial improvements. We also conduct a qualitative analysis to gauge where the ceiling of the task is in a realistic setup.}, address = {Valencia, Spain}, author = {de Kok, Dani{\"e}l and Ma, Jianqiang and Dima, Corina and Hinrichs, Erhard}, booktitle = {Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers}, editor = {Lapata, Mirella and Blunsom, Phil and Koller, Alexander}, month = {04}, pages = {311--317}, publisher = {Association for Computational Linguistics}, title = {{PP} Attachment: Where do We Stand?}, url = {https://aclanthology.org/E17-2050}, year = 2017 }
2. de Kok, D., Dima, C., Ma, J., & Hinrichs, E. W. (2017). Extracting a PP Attachment Data Set from a German Dependency Treebank Using Topological Fields. In M. Dickinson, J. Hajic, S. Kübler, & A. Przepiórkowski (Eds.), Proceedings of the 15th International Workshop on Treebanks and Linguistic Theories (TLT15), Bloomington, IN, USA, January 20-21, 2017 (Vol. 1779, pp. 89--98). CEUR-WS.org. https://ceur-ws.org/Vol-1779/07dekok.pdf
  - BibTeX
  BibTeX
  @inproceedings{DBLP:conf/tlt/KokDMH17, author = {de Kok, Dani{\"{e}}l and Dima, Corina and Ma, Jianqiang and Hinrichs, Erhard W.}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {Proceedings of the 15th International Workshop on Treebanks and Linguistic Theories (TLT15), Bloomington, IN, USA, January 20-21, 2017}, editor = {Dickinson, Markus and Hajic, Jan and K{\"{u}}bler, Sandra and Przepi{\'{o}}rkowski, Adam}, pages = {89--98}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, title = {Extracting a {PP} Attachment Data Set from a German Dependency Treebank Using Topological Fields}, url = {https://ceur-ws.org/Vol-1779/07dekok.pdf}, volume = 1779, year = 2017 }
3. de Kok, D., Fischer, P., Dima, C., & Hinrichs, E. (2017). Distributional regularities of verbs and verbal adjectives: Treebank evidence and broader implications. In J. Hajic (Ed.), Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories (pp. 1--9). https://aclanthology.org/W17-7603
  - BibTeX
  BibTeX
  @inproceedings{de-kok-etal-2017-distributional, address = {Prague, Czech Republic}, author = {de Kok, Dani{\"e}l and Fischer, Patricia and Dima, Corina and Hinrichs, Erhard}, booktitle = {Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories}, editor = {Haji{\v{c}}, Jan}, pages = {1--9}, title = {Distributional regularities of verbs and verbal adjectives: Treebank evidence and broader implications}, url = {https://aclanthology.org/W17-7603}, year = 2017 }
4. Hernández, D., & Gutiérrez, C. (2017). Semantics for Querying Paths in Graph Databases: No-repeated-node or No-repeated-edge? In J. L. Reutter & D. Srivastava (Eds.), Proceedings of the 11th Alberto Mendelzon International Workshop on Foundations of Data Management and the Web, Montevideo, Uruguay, June 7-9, 2017. (Vol. 1912). CEUR-WS.org. http://ceur-ws.org/Vol-1912/paper13.pdf
  - BibTeX
  BibTeX
  @inproceedings{DBLP:conf/amw/HernandezG17, author = {Hernández, Daniel and Gutiérrez, Claudio}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {Proceedings of the 11th Alberto Mendelzon International Workshop on Foundations of Data Management and the Web, Montevideo, Uruguay, June 7-9, 2017.}, editor = {Reutter, Juan L. and Srivastava, Divesh}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, title = {Semantics for Querying Paths in Graph Databases: No-repeated-node or No-repeated-edge?}, url = {http://ceur-ws.org/Vol-1912/paper13.pdf}, volume = 1912, year = 2017 }
2016
1. Dima, C. (2016). On the Compositionality and Semantic Interpretation of English Noun Compounds. In P. Blunsom, K. Cho, S. Cohen, E. Grefenstette, K. M. Hermann, L. Rimell, J. Weston, & S. W. Yih (Eds.), Proceedings of the 1st Workshop on Representation Learning for NLP (pp. 27--39). Association for Computational Linguistics. https://doi.org/10.18653/v1/W16-1604
  - BibTeX
  BibTeX
  @inproceedings{dima-2016-compositionality, address = {Berlin, Germany}, author = {Dima, Corina}, booktitle = {Proceedings of the 1st Workshop on Representation Learning for {NLP}}, doi = {10.18653/v1/W16-1604}, editor = {Blunsom, Phil and Cho, Kyunghyun and Cohen, Shay and Grefenstette, Edward and Hermann, Karl Moritz and Rimell, Laura and Weston, Jason and Yih, Scott Wen-tau}, month = {08}, pages = {27--39}, publisher = {Association for Computational Linguistics}, title = {On the Compositionality and Semantic Interpretation of {E}nglish Noun Compounds}, url = {https://aclanthology.org/W16-1604}, year = 2016 }
2. Gutiérrez, C., Hernández, D., Hogan, A., & Polleres, A. (2016). Certain Answers for SPARQL? In R. Pichler & A. S. da Silva (Eds.), Proceedings of the 10th Alberto Mendelzon International Workshop on Foundations of Data Management, Panama City, Panama, May 8-10, 2016. (Vol. 1644). CEUR-WS.org. http://ceur-ws.org/Vol-1644/paper13.pdf
  - BibTeX
  BibTeX
  @inproceedings{DBLP:conf/amw/GutierrezHHP16, author = {Gutiérrez, Claudio and Hernández, Daniel and Hogan, Aidan and Polleres, Axel}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {Proceedings of the 10th Alberto Mendelzon International Workshop on Foundations of Data Management, Panama City, Panama, May 8-10, 2016.}, editor = {Pichler, Reinhard and da Silva, Altigran Soares}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, title = {Certain Answers for SPARQL?}, url = {http://ceur-ws.org/Vol-1644/paper13.pdf}, volume = 1644, year = 2016 }
3. Hernández, D., Hogan, A., Riveros, C., Rojas, C., & Zerega, E. (2016). Querying Wikidata: Comparing SPARQL, Relational and Graph Databases. In P. T. Groth, E. Simperl, A. J. G. Gray, M. Sabou, M. Krötzsch, F. Lécué, F. Flöck, & Y. Gil (Eds.), The Semantic Web - ISWC 2016 - 15th International Semantic Web Conference, Kobe, Japan, October 17-21, 2016, Proceedings, Part II (Vol. 9982, pp. 88--103). https://doi.org/10.1007/978-3-319-46547-0_10
  - BibTeX
  BibTeX
  @inproceedings{DBLP:conf/semweb/HernandezHRRZ16, author = {Hernández, Daniel and Hogan, Aidan and Riveros, Cristian and Rojas, Carlos and Zerega, Enzo}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {The Semantic Web - {ISWC} 2016 - 15th International Semantic Web Conference, Kobe, Japan, October 17-21, 2016, Proceedings, Part {II}}, doi = {10.1007/978-3-319-46547-0_10}, editor = {Groth, Paul T. and Simperl, Elena and Gray, Alasdair J. G. and Sabou, Marta and Kr{\"{o}}tzsch, Markus and L{\'{e}}cu{\'{e}}, Freddy and Fl{\"{o}}ck, Fabian and Gil, Yolanda}, pages = {88--103}, series = {Lecture Notes in Computer Science}, title = {Querying Wikidata: Comparing SPARQL, Relational and Graph Databases}, url = {https://doi.org/10.1007/978-3-319-46547-0_10}, volume = 9982, year = 2016 }
4. Niepert, M., Ahmed, M., & Kutzkov, K. (2016). Learning Convolutional Neural Networks for Graphs. In M.-F. Balcan & K. Q. Weinberger (Eds.), Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016 (Vol. 48, pp. 2014--2023). JMLR.org. http://proceedings.mlr.press/v48/niepert16.html
  - BibTeX
  BibTeX
  @inproceedings{DBLP:conf/icml/NiepertAK16, author = {Niepert, Mathias and Ahmed, Mohamed and Kutzkov, Konstantin}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {Proceedings of the 33nd International Conference on Machine Learning, {ICML} 2016, New York City, NY, USA, June 19-24, 2016}, editor = {Balcan, Maria{-}Florina and Weinberger, Kilian Q.}, pages = {2014--2023}, publisher = {JMLR.org}, series = {{JMLR} Workshop and Conference Proceedings}, title = {Learning Convolutional Neural Networks for Graphs}, url = {http://proceedings.mlr.press/v48/niepert16.html}, volume = 48, year = 2016 }
2015
1. Dima, C. (2015). Reverse-engineering Language: A Study on the Semantic Compositionality of German Compounds. In L. Màrquez, C. Callison-Burch, & J. Su (Eds.), Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (pp. 1637--1642). Association for Computational Linguistics. https://doi.org/10.18653/v1/D15-1188
  - BibTeX
  BibTeX
  @inproceedings{dima-2015-reverse, address = {Lisbon, Portugal}, author = {Dima, Corina}, booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing}, doi = {10.18653/v1/D15-1188}, editor = {M{\`a}rquez, Llu{\'\i}s and Callison-Burch, Chris and Su, Jian}, month = {09}, pages = {1637--1642}, publisher = {Association for Computational Linguistics}, title = {Reverse-engineering Language: A Study on the Semantic Compositionality of {G}erman Compounds}, url = {https://aclanthology.org/D15-1188}, year = 2015 }
2. Sorokin, D., Dima, C., & Hinrichs, E. (2015). Classifying Semantic Relations in German Nominal Compounds using a Hybrid Annotation Scheme. Journal of Cognitive Science, 16(3), Article 3.
  - BibTeX
  BibTeX
  @article{10.17791/JCS.2015.16.3.261, author = {Sorokin, Daniil and Dima, Corina and Hinrichs, Erhard}, journal = {Journal of Cognitive Science}, month = {09}, number = 3, pages = {261-286}, publisher = {인지과학연구소}, title = {Classifying Semantic Relations in German Nominal Compounds using a Hybrid Annotation Scheme}, volume = 16, year = 2015 }
3. Dima, C., & Hinrichs, E. (2015). Automatic Noun Compound Interpretation using Deep Neural Networks and Word Embeddings. In M. Purver, M. Sadrzadeh, & M. Stone (Eds.), Proceedings of the 11th International Conference on Computational Semantics (pp. 173--183). Association for Computational Linguistics. https://aclanthology.org/W15-0122
  - BibTeX
  BibTeX
  @inproceedings{dima-hinrichs-2015-automatic, address = {London, UK}, author = {Dima, Corina and Hinrichs, Erhard}, booktitle = {Proceedings of the 11th International Conference on Computational Semantics}, editor = {Purver, Matthew and Sadrzadeh, Mehrnoosh and Stone, Matthew}, month = {04}, pages = {173--183}, publisher = {Association for Computational Linguistics}, title = {Automatic Noun Compound Interpretation using Deep Neural Networks and Word Embeddings}, url = {https://aclanthology.org/W15-0122}, year = 2015 }
4. Hernández, D., & Gutiérrez, C. (2015). Disentangling the Notion of Dataset in SPARQL. In A. Cali & M.-E. Vidal (Eds.), Proceedings of the 9th Alberto Mendelzon International Workshop on Foundations of Data Management, Lima, Peru, May 6 - 8, 2015. (Vol. 1378). CEUR-WS.org. http://ceur-ws.org/Vol-1378/AMW_2015_paper_41.pdf
  - BibTeX
  BibTeX
  @inproceedings{DBLP:conf/amw/HernandezG15, author = {Hernández, Daniel and Gutiérrez, Claudio}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {Proceedings of the 9th Alberto Mendelzon International Workshop on Foundations of Data Management, Lima, Peru, May 6 - 8, 2015.}, editor = {Cali, Andrea and Vidal, Maria-Esther}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, title = {Disentangling the Notion of Dataset in {SPARQL}}, url = {http://ceur-ws.org/Vol-1378/AMW_2015_paper_41.pdf}, volume = 1378, year = 2015 }
5. Hernández, D., Hogan, A., & Krötzsch, M. (2015). Reifying RDF: What Works Well With Wikidata? In T. Liebig & A. Fokoue (Eds.), Proceedings of the 11th International Workshop on Scalable Semantic Web Knowledge Base Systems co-located with 14th International Semantic Web Conference (ISWC 2015), Bethlehem, PA, USA, October 11, 2015. (Vol. 1457, pp. 32–47). CEUR-WS.org. http://ceur-ws.org/Vol-1457/SSWS2015_paper3.pdf
  - BibTeX
  BibTeX
  @inproceedings{DBLP:conf/semweb/HernandezHK15, abstract = {In this paper, we compare various options for reifying RDF triples. We are motivated by the goal of representing Wikidata as RDF, which would allow legacy Semantic Web languages, techniques and tools – for example, SPARQL engines – to be used for Wikidata. However, Wikidata annotates statements with qualifiers and references, which require some notion of reification to model in RDF. We thus investigate four such options: (1) standard reification, (2) n-ary relations, (3) singleton properties, and (4) named graphs. Taking a recent dump of Wikidata, we generate the four RDF datasets pertaining to each model and discuss high-level aspects relating to data sizes, etc. To empirically compare the effect of the different models on query times, we collect a set of benchmark queries with four model-specific versions of each query. We present the results of running these queries against five popular SPARQL implementations: 4store, BlazeGraph, GraphDB, Jena TDB and Virtuoso.}, author = {Hernández, Daniel and Hogan, Aidan and Krötzsch, Markus}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {Proceedings of the 11th International Workshop on Scalable Semantic Web Knowledge Base Systems co-located with 14th International Semantic Web Conference (ISWC 2015), Bethlehem, PA, USA, October 11, 2015.}, editor = {Liebig, Thorsten and Fokoue, Achille}, eventdate = {October 11, 2015}, eventtitle = {11th International Workshop on Scalable Semantic Web Knowledge Base Systems}, language = {English}, pages = {32-47}, publisher = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, title = {Reifying RDF: What Works Well With Wikidata?}, url = {http://ceur-ws.org/Vol-1457/SSWS2015_paper3.pdf}, venue = {Bethlehem, PA, USA}, volume = 1457, year = 2015 }
2014
1. Dima, C., Henrich, V., Hinrichs, E., & Hoppermann, C. (2014). How to Tell a Schneemann from a Milchmann: An Annotation Scheme for Compound-Internal Relations. In N. Calzolari, K. Choukri, T. Declerck, H. Loftsson, B. Maegaard, J. Mariani, A. Moreno, J. Odijk, & S. Piperidis (Eds.), Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14) (pp. 1194--1201). European Language Resources Association (ELRA). http://www.lrec-conf.org/proceedings/lrec2014/pdf/329_Paper.pdf
  - BibTeX
  BibTeX
  @inproceedings{dima-etal-2014-tell, abstract = {This paper presents a language-independent annotation scheme for the semantic relations that link the constituents of noun-noun compounds, such as Schneemann {`}snow man{'} or Milchmann {`}milk man{'}. The annotation scheme is hybrid in the sense that it assigns each compound a two-place label consisting of a semantic property and a prepositional paraphrase. The resulting inventory combines the insights of previous annotation schemes that rely exclusively on either semantic properties or prepositions, thus avoiding the known weaknesses that result from using only one of the two label types. The proposed annotation scheme has been used to annotate a set of 5112 German noun-noun compounds. A release of the dataset is currently being prepared and will be made available via the CLARIN Center T{\"u}bingen. In addition to the presentation of the hybrid annotation scheme, the paper also reports on an inter-annotator agreement study that has resulted in a substantial agreement among annotators.}, address = {Reykjavik, Iceland}, author = {Dima, Corina and Henrich, Verena and Hinrichs, Erhard and Hoppermann, Christina}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)}, editor = {Calzolari, Nicoletta and Choukri, Khalid and Declerck, Thierry and Loftsson, Hrafn and Maegaard, Bente and Mariani, Joseph and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios}, month = {05}, pages = {1194--1201}, publisher = {European Language Resources Association (ELRA)}, title = {How to Tell a Schneemann from a Milchmann: An Annotation Scheme for Compound-Internal Relations}, url = {http://www.lrec-conf.org/proceedings/lrec2014/pdf/329_Paper.pdf}, year = 2014 }
2. Dima, C. (2014). Answering Natural Language Questions with Intui3. In L. Cappellato, N. Ferro, M. Halvey, & W. Kraaij (Eds.), Working Notes for CLEF 2014 Conference, Sheffield, UK, September 15-18, 2014 (Vol. 1180, pp. 1201--1211). CEUR-WS.org. https://ceur-ws.org/Vol-1180/CLEF2014wn-QA-Dima2014.pdf
  - BibTeX
  BibTeX
  @inproceedings{DBLP:conf/clef/Dima14, author = {Dima, Corina}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {Working Notes for {CLEF} 2014 Conference, Sheffield, UK, September 15-18, 2014}, editor = {Cappellato, Linda and Ferro, Nicola and Halvey, Martin and Kraaij, Wessel}, pages = {1201--1211}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, title = {Answering Natural Language Questions with Intui3}, url = {https://ceur-ws.org/Vol-1180/CLEF2014wn-QA-Dima2014.pdf}, volume = 1180, year = 2014 }
2013
1. Dima, C. (2013). Intui2: A Prototype System for Question Answering over Linked Data. In P. Forner, R. Navigli, D. Tufis, & N. Ferro (Eds.), Working Notes for CLEF 2013 Conference , Valencia, Spain, September 23-26, 2013 (Vol. 1179). CEUR-WS.org. https://ceur-ws.org/Vol-1179/CLEF2013wn-QALD3-Dima2013.pdf
  - BibTeX
  BibTeX
  @inproceedings{DBLP:conf/clef/Dima13, author = {Dima, Corina}, bibsource = {dblp computer science bibliography, https://dblp.org}, booktitle = {Working Notes for {CLEF} 2013 Conference , Valencia, Spain, September 23-26, 2013}, editor = {Forner, Pamela and Navigli, Roberto and Tufis, Dan and Ferro, Nicola}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, title = {Intui2: {A} Prototype System for Question Answering over Linked Data}, url = {https://ceur-ws.org/Vol-1179/CLEF2013wn-QALD3-Dima2013.pdf}, volume = 1179, year = 2013 }
2012
1. Culy, C., Dima, C., & Dima, E. (2012). Through the Looking Glass: Two Approaches to Visualizing Linguistic Syntax Trees. 2012 16th International Conference on Information Visualisation, 214–219. https://doi.org/10.1109/IV.2012.44
  - BibTeX
  BibTeX
  @inproceedings{6295816, author = {Culy, Chris and Dima, Corina and Dima, Emanuel}, booktitle = {2012 16th International Conference on Information Visualisation}, doi = {10.1109/IV.2012.44}, pages = {214-219}, title = {Through the Looking Glass: Two Approaches to Visualizing Linguistic Syntax Trees}, year = 2012 }
2011
1. Dima, C., & Hinrichs, E. (2011). A Semi-Automatic, Iterative Method for Creating a Domain-Specific Treebank. In R. Mitkov & G. Angelova (Eds.), Proceedings of the International Conference Recent Advances in Natural Language Processing 2011 (pp. 413--419). Association for Computational Linguistics. https://aclanthology.org/R11-1057
  - BibTeX
  BibTeX
  @inproceedings{dima-hinrichs-2011-semi, address = {Hissar, Bulgaria}, author = {Dima, Corina and Hinrichs, Erhard}, booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing 2011}, editor = {Mitkov, Ruslan and Angelova, Galia}, month = {09}, pages = {413--419}, publisher = {Association for Computational Linguistics}, title = {A Semi-Automatic, Iterative Method for Creating a Domain-Specific Treebank}, url = {https://aclanthology.org/R11-1057}, year = 2011 }
2009
1. Cristea, D., Dima, E., & Dima, C. (2009). Why Would a Robot Make Use of Pronouns? An Evolutionary Investigation of the Emergence of Pronominal Anaphora. In S. Lalitha Devi, A. Branco, & R. Mitkov (Eds.), Anaphora Processing and Applications (pp. 1--14). Springer Berlin Heidelberg.
  - BibTeX
  BibTeX
  @inproceedings{10.1007/978-3-642-04975-0_1, abstract = {We investigate whether and in what conditions pronominal anaphora could be acquired by intelligent agents as a means to express recently mentioned entities. The use of pronouns is conditioned by the existence of a memory recording the object previously in focus. The approach follows an evolutionary paradigm of language acquisition. Experiments show that pronouns can be easily included in a vocabulary of a community of 10 agents dialoguing on a static scene and that, generally, they enhance the communication success.}, address = {Berlin, Heidelberg}, author = {Cristea, Dan and Dima, Emanuel and Dima, Corina}, booktitle = {Anaphora Processing and Applications}, editor = {Lalitha Devi, Sobha and Branco, Ant{\'o}nio and Mitkov, Ruslan}, isbn = {978-3-642-04975-0}, pages = {1--14}, publisher = {Springer Berlin Heidelberg}, title = {Why Would a Robot Make Use of Pronouns? An Evolutionary Investigation of the Emergence of Pronominal Anaphora}, year = 2009 }

For further information about our Research Topics, please visit:

Analytic Computing

Intelligent Sensing and Perception

Machine Learning for Simulation Science

Socially Intelligent Robotics

Information Systems (ELLIS-SimTech Research Group)

Head of Department

Prof. Dr. Steffen Staab

Managing Director

Phone: +49 711 685 88100

E-Mail

Prof. Dr. Mathias Niepert

Director

Phone: +49 711 685 88122

E-Mail

Prof. Dr. Kai Arras

Director

Phone: +49 711 685 88135

E-Mail

Jun.-Prof Dr.-Ing Alina Roitberg

Director

Phone: +49 711 685 88160

E-Mail

Research Group Leader

Luiz Chamon

ELLIS-SimTech Independent Junior Research Group Leader

E-Mail

Research

Area

Applications

Institute Publications

2024

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

2023

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

BibTeX

2022