Publications

Insitute for Artificial Intelligence (KI)

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

Institute Publications

  1. 2025

    1. He, Y., Xiong, B., Hernández, D., Zhu, Y., Kharlamov, E., & Staab, S. (2025). DAGE: DAG Query Answering via Relational Combinator with Logical Constraints. THE WEB CONFERENCE 2025. https://openreview.net/forum?id=x1nXBzUknn
  2. 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
    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
    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/
    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
    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
    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
    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.
    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
    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
    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
    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.
    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
    13. "Hosseini, A. S., & "Staab, S. (2024). Disambiguating Emotional Connotations of Words Using Contextualized Word Representations.
    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
    15. Chamon, L. F. O., Karimi, M. R., & Korba, A. (2024). Constrained Sampling with Primal-Dual Langevin Monte Carlo. In Proceedings of the 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024). https://doi.org/10.48550/arXiv.2411.00568
    16. 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
    17. 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.
    18. 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
    19. 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.
    20. 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
    21. 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).
    22. 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
    23. Hedeshy, R., Menges, R., & Staab, S. (2024). Raw audio samples of the CNVVE dataset. DaRUS. https://doi.org/10.18419/DARUS-3897
    24. Hedeshy, R., Menges, R., & Staab, S. (2024). CNVVE Dataset clean audio samples. DaRUS. https://doi.org/10.18419/DARUS-3898
    25. 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
    26. Liu, X., Liu, A., den Broeck, G. V., & Liang, Y. (2024). A Tractable Inference Perspective of Offline RL. In Proceedings of the 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024). https://doi.org/10.48550/arXiv.2311.00094
    27. Manolache, A., Tantaru, D., & Niepert, M. (2024). MolMix: A Simple Yet Effective Baseline for Multimodal Molecular Representation Learning. https://doi.org/10.48550/arXiv.2410.07981
    28. Mougan, C., Ruggieri, S., State, L., Ferrara, A., & Staab, S. (2024). Measuring the Impact of Equal Treatment as Blindness via Explanations Disparity. https://openreview.net/forum?id=ndU9EvrVBH
    29. Musekamp, D., Kalimuthu, M., Holzmüller, D., Takamoto, M., & Niepert, M. (2024). Active Learning for Neural PDE Solvers. NeurIPS 2024 Workshop on Data-Driven and Differentiable Simulations, Surrogates, and Solvers. https://openreview.net/forum?id=LD63WlGRQQ
    30. 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
    31. 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
    32. 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,.
    33. 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
    34. Peng, K., Wen, D., Yang, K., Luo, A., Chen, Y., Fu, J., Sarfraz, M. S., Roitberg, A., & Stiefelhagen, R. (2024). Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain Scheduler. In Proceedings of the 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024). https://doi.org/10.48550/arXiv.2409.17555
    35. 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
    36. 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
    37. Qian, C., Manolache, A., Morris, C., & Niepert, M. (2024). Probabilistic Graph Rewiring via Virtual Nodes. In Proceedings of the 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024). https://doi.org/10.48550/arXiv.2405.17311
    38. 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
    39. Seifer, P., Hernández, D., Lämmel, R., & Staab, S. (2024). Code for From Shapes to Shapes. https://doi.org/10.18419/darus-3977
    40. Serra, G., & Niepert, M. (2024). L2XGNN: Learning to Explain Graph Neural Networks. Machine Learning Journal. https://arxiv.org/abs/2209.14402
    41. 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
    42. Torres, E., & Niepert, M. (2024). Survey: Adaptive Physics-informed Neural Networks. Neurips 2024 Workshop Foundation Models for Science: Progress, Opportunities, and Challenges. https://openreview.net/forum?id=bYP6YB84Pq
    43. Tran, H.-C., Nguyen, D. M. H., Nguyen, D. M., Nguyen, T.-T., Le, N., Xie, P., Sonntag, D., Zou, J. Y., Nguyen, B. T., & Niepert, M. (2024). Accelerating Transformers with Spectrum-Preserving Token Merging. In Proceedings of the 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024). https://doi.org/10.48550/arXiv.2405.16148
    44. Wang, Z., Cai, S., Mu, Z., Lin, H., Zhang, C., Liu, X., Li, Q., Liu, A., Ma, X., & Liang, Y. (2024). OmniJARVIS: Unified Vision-Language-Action Tokenization Enables Open-World Instruction Following Agents. In Proceedings of the 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024). https://doi.org/10.48550/arXiv.2407.00114
    45. 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
    46. 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
    47. 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
    48. 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
    49. 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
To the top of the page