This image shows Bo Xiong

Bo Xiong

Dr.

Researcher
KI
Analytic Computing

Contact

+49 711 685 88110
Business card (VCF)

Universitätsstraße 32
70569 Stuttgart
Germany
Room: 2.303b

Subject

Machine learning and knowledge graphs

If you are a master student and are interested in working on a thesis that involves ML on graph data, please do get in touch. 

  1. Zhu, Y., Potyka, N., Hern’andez, D., He, Y., Ding, Z., Xiong, B., Zhou, D., Kharlamov, E., & Staab, S. (2025). ArgRAG: Explainable Retrieval Augmented Generation using Quantitative Bipolar Argumentation. Proceedings of Machine Learning Research in 19th Conference on Neurosymbolic Learning and Reasoning, 284, 1–22. https://arxiv.org/abs/2508.20131
  2. 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://doi.org/10.1145/3696410.3714677
  3. Jin, M., Shi, G., Li, Y.-F., Xiong, B., Zhou, T., Salim, F. D., Zhao, L., Wu, L., Wen, Q., & Pan, S. (2025). Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting. IEEE transactions on pattern analysis and machine intelligence, 47, Article 6. https://doi.org/10.1109/TPAMI.2025.3545671
  4. Gregucci, C., Xiong, B., Hernandez, D., Loconte, L., Minervini, P., Staab, S., & Vergari, A. (2025, July). Is Complex Query Answering Really Complex? Forty-Second International Conference on Machine Learning. https://doi.org/10.48550/arXiv.2410.12537
  5. Zhu, Y., Hernández, D., He, Y., Ding, Z., Xiong, B., Kharlamov, E., & Staab, S. (2025). Predicate-Conditional Conformalized Answer Sets for Knowledge Graph Embeddings. In W. Che, J. Nabende, E. Shutova, & M. T. Pilehvar (Eds.), Findings of the Association for Computational Linguistics, ACL 2025 (pp. 4145–4167). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.findings-acl.215
  6. Jia, Y., Song, Y., Xiong, B., Cheng, J., Zhang, W., Yang, S. X., & Kwong, S. (2024). Hierarchical Perception-Improving for Decentralized Multi-Robot Motion Planning in Complex Scenarios. IEEE transactions on intelligent transportation systems, 25, Article 7. https://doi.org/10.1109/TITS.2023.3344518
  7. Xiong, B., Potyka, N., Tran, T.-K., Nayyeri, M., & Staab, S. (2024). Code for Faithful Embeddings for EL++ Knowledge Bases. https://doi.org/10.18419/darus-3989
  8. Xiong, B., Zhu, S., Potyka, N., Pan, S., Zhou, C., & Staab, S. (2024). Code for Pseudo-Riemannian Graph Convolutional Networks. https://doi.org/10.18419/darus-4340
  9. 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. 2024 IEEE 40th International Conference on Data Engineering (ICDE), 1310–1323. https://doi.org/10.1109/ICDE60146.2024.00108
  10. Xiong, B. (2024). Geometric relational embeddings [Dissertation, Universität Stuttgart]. https://doi.org/10.18419/opus-15257
  11. He, Y., Hernández, D., Nayyeri, M., Xiong, B., Zhu, Y., Kharlamov, E., & Staab, S. (2024). Generating SROI^- Ontologies via Knowledge Graph Query Embedding Learning. https://doi.org/10.48550/arXiv.2407.09212
  12. 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. In K. Duh, H. Gomez, & S. Bethard (Eds.), Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Vol. 1 : Long Papers (pp. 1877–1895). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.naacl-long.104
  13. He, Y., Hernandez, D., Nayyeri, M., Xiong, B., Zhu, Y., Kharlamov, E., & Staab, S. (2024). Generating SROI- Ontologies via Knowledge Graph Query Embedding Learning. Ecai 2024 : 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain - Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024), Article 392. https://doi.org/10.3233/faia241002
  14. 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). https://doi.org/10.18419/darus-3978
  15. Xiong, B., Nayyeri, M., Luo, L., Wang, Z., Pan, S., & Staab, S. (2024). NestE : Modeling Nested Relational Structures for Knowledge Graph Reasoning. Proceedings of the 38th AAAI Conference on Artificial Intelligence, Article 38, 8. https://doi.org/10.1609/aaai.v38i8.28772
  16. Zhou, D., Yang, H., Xiong, B., Ma, Y., & Kharlamov, E. (2024). Alleviating Over-Smoothing via Aggregation over Compact Manifolds. In D.-N. Yang, X. Xie, V. S. Tseng, J. Pei, J.-W. Huang, & J. C.-W. Lin (eds.), Advances in Knowledge Discovery and Data Mining : 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7–10, 2024, Proceedings, Part II: Vol. II (No. 14646; pp. 390–404). Springer. https://doi.org/10.1007/978-981-97-2253-2_31
  17. Xiong, B., Nayyeri, M., Pan, S., & Staab, S. (2024). Code for Shrinking Embeddings for Hyper-relational Knowledge Graphs. https://doi.org/10.18419/darus-3979
  18. Xiong, B., Nayyeri, M., Cochez, M., & Staab, S. (2024). Code for Hyperbolic Embedding Inference for Structured Multi-Label Prediction. https://doi.org/10.18419/darus-3988
  19. Xiong, B., Zhu, S., Nayyeri, M., Xu, C., Pan, S., & Staab, S. (2024). Code for Ultrahyperbolic Knowledge Graph Embeddings. https://doi.org/10.18419/darus-4342
  20. Ding, Z., Wu, J., Wu, J., Xia, Y., Xiong, B., & Tresp, V. (2024). Temporal Fact Reasoning over Hyper-Relational Knowledge Graphs. In Y. Al-Onaizan, M. Bansal, & Y.-N. Chen (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2024, Miami, Florida, USA, November 12-16, 2024 (pp. 355–373). Association for Computational Linguistics. https://aclanthology.org/2024.findings-emnlp.20
  21. Nayyeri, M., Xiong, B., Mohammadi, M., Akter, M. M., Alam, M. M., Lehmann, J., & Staab, S. (2023). Knowledge Graph Embeddings using Neural Ito Process : From Multiple Walks to Stochastic Trajectories. In A. Rogers, J. Boyd-Graber, & N. Okazaki (eds.), Findings of the Association for Computational Linguistics : ACL 2023 (pp. 7165–7179). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-acl.448
  22. Xiong, B., Nayyeri, M., Pan, S., & Staab, S. (2023). Shrinking Embeddings for Hyper-Relational Knowledge Graphs. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, 1 : Long Papers. https://doi.org/10.18653/v1/2023.acl-long.743
  23. Xiong, B., Nayyeri, M., Daza, D., & Cochez, M. (2023). Reasoning beyond Triples : Recent Advances in Knowledge Graph Embeddings. Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 5228–5231. https://doi.org/10.1145/3583780.3615294
  24. Lu, J., Shen, J., Xiong, B., Ma, W., Staab, S., & Yang, C. (2023). HiPrompt : Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting. SIGIR ’23 : Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2052–2056. https://doi.org/10.1145/3539618.3591997
  25. Zhu, Y., Potyka, N., Xiong, B., Tran, T.-K., Nayyeri, M., Staab, S., & Kharlamov, E. (2023). Towards Statistical Reasoning with Ontology Embeddings3. In I. Fundulaki, K. Kozaki, D. Gariko, & J. M. Gomez-Perez (eds.), ISWC-Posters-Demos-Industry 2023 : Posters, Demos, and Industry Tracks at ISWC 2023 (No. 3632). RWTH Aachen. https://ceur-ws.org/Vol-3632/ISWC2023_paper_442.pdf
  26. He, Y., Nayyeri, M., Xiong, B., Zhu, Y., Kharlamov, E., & Staab, S. (2023). Can Pattern Learning Enhance Complex Logical Query Answering? In I. Fundulaki, K. Kozaki, D. Gariko, & J. M. Gomez-Perez (eds.), ISWC-Posters-Demos-Industry 2023 : Posters, Demos, and Industry Tracks at ISWC 2023 (No. 3632). RWTH Aachen. https://ceur-ws.org/Vol-3632/ISWC2023_paper_463.pdf
  27. Zhou, M., Yang, M., Xiong, B., Xiong, H., & King, I. (2023). Hyperbolic Graph Neural Networks : A Tutorial on Methods and Applications. KDD ’23 : Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/3580305.3599562
  28. Xiong, B., Zhu, S., Potyka, N., Pan, S., Zhou, C., & Staab, S. (2022). Pseudo-Riemannian Graph Convolutional Networks. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (eds.), Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (pp. 3488–3501). Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2022/hash/16c628ab12dc4caca8e7712affa6c767-Abstract-Conference.html
  29. Xiong, B., Cochez, M., Nayyeri, M., & Staab, S. (2022). Hyperbolic Embedding Inference for Structured Multi-Label Prediction. 36th Conference on Neural Information Processing Systems (NeurIPS 2022). https://openreview.net/forum?id=XFnDhcEH9FF
  30. Xiong, B., Potyka, N., Tran, T.-K., Nayyeri, M., & Staab, S. (2022). Faithful Embeddings for EL++ Knowledge Bases. In U. Sattler, A. Hogan, M. Keet, V. Presutti, J. P. A. Almeida, H. Takeda, P. Monnin, G. Pirrò, & C. D’Amato (eds.), The Semantic Web : ISWC 2022 (No. 13489; pp. 22–38). Springer. https://doi.org/10.1007/978-3-031-19433-7_2
  31. Xiong, B., Zhu, S., Nayyeri, M., Xu, C., Pan, S., Zhou, C., & Staab, S. (2022). Ultrahyperbolic Knowledge Graph Embeddings. KDD ’22 : Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2130–2139. https://doi.org/10.1145/3534678.3539333
  32. Xu, C., Su, F., Xiong, B., & Lehmann, J. (2022). Time-aware Entity Alignment using Temporal Relational Attention. WWW ’22 : Proceedings of the ACM Web Conference 2022, 788–797. https://doi.org/10.1145/3485447.3511922
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