KnowGraphs

Knowledge Graphs at Scale

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Knowledge graphs (KGs) are a flexible knowledge representation paradigm intended to allow knowledge to be consumed by humans and machines. KGs are widely regarded as a key enabler for a number of increasingly popular technologies including Web search, question answering, personal assistants and AI across most sectors including Industry 4.0, personalized medicine, legislation, economics and more. KGs are now used by several large companies as a key component of their data products. However, while they are rightly praised as a key technology for all future data-driven enterprises and regarded as a promising approach towards “blurring the lines between human and machine”, KGs are currently unattainable for the majority of companies and users.
The objective of KnowGraphs is to scale KGs to be accessible to a wide audience of users across multiple domains including companies (in domains including Industry 4.0, biomedicine, finance, law) of all sizes and even end users (e.g., through personal assistants and web search). Addressing this goal demands a mix of works from the theoretical foundations to the exploitation and economic repercussions of knowledge graphs.

Operating Time: 10/2019 - 09/2023

Source of Funding: EU Horizon 2020 - Grant agreement ID 860801.

Partners:

Web Site: KnowGraphs

Publications

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. He, Y., Nayyeri, M., Xiong, B., Zhu, Y., Kharlamov, E., & Staab, S. (2023). Can Pattern Learning Enhance Complex Logical Query Answering?
  8. 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
  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
  10. 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
  11. 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, 1. https://doi.org/10.18653/v1/2023.acl-long.743
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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

Data and Software

  1. 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

Project Members

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