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Insitute for Artificial Intelligence (KI)

Here you can find all the publications from the Institute for Artificial Intelligence.

Institute Publications

2024
1. 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
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  @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 }
2. 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
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  @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 }
3. 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
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  @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 }
4. 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.
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  @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 }
5. 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
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  @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 }
6. 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
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  @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 }
7. 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
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  @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 }
8. 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.
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  @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 }
9. 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
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  @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 }
10. 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
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  @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 }
11. 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
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  @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 }
12. 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,.
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  @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 }
13. 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
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  @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 }
14. 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
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  @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 }
15. Seifer, P., Hernández, D., Lämmel, R., & Staab, S. (2024). Code for From Shapes to Shapes. https://doi.org/10.18419/darus-3977
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  @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 }
16. 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
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  @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 }
17. 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
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  @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 }
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
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  @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
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  @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
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  @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
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  @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
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  @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
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  @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
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  @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
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  @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
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  @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
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  @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
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  @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.
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  @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
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  @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
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  @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
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  @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
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  @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
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  @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
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  @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
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  @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).
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  @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
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  @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.
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  @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
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  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
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  @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
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  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
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  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).
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  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
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  @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
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  @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
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  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.
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  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
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  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 }

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