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publications

A Question of Norton-Sullivan in the Analytic Case

Published in International Mathematics Research Notices (IMRN 2021), 2021

This paper addresses a question of Norton-Sullivan in the context of analytic cases.

Recommended citation: Wang, J., & Yang, H. (2021). "A Question of Norton-Sullivan in the Analytic Case." International Mathematics Research Notices, 2021(21), 17201–17219. https://doi.org/10.1093/imrn/rnz231

Union and Intersection of All Justifications

Published in European Semantic Web Conference (ESWC 2022), 2022

This paper examines the union and intersection of all justifications in ontology systems.

Recommended citation: Chen, J., Ma, Y., Peñaloza, R., & Yang, H. (2022). "Union and Intersection of All Justifications." European Semantic Web Conference, 56–73. https://doi.org/10.1007/978-3-031-06981-9_4

Hypergraph-Based Inference Rules for Computing Ontology Justifications

Published in International Joint Conference on Automated Reasoning (IJCAR 2022), 2022

This paper introduces hypergraph-based inference rules for computing justifications in ontologies.

Recommended citation: Yang, H., Ma, Y., & Bidoit, N. (2022). "Hypergraph-Based Inference Rules for Computing-Ontology Justifications." International Joint Conference on Automated Reasoning, 310–328. https://doi.org/10.1007/978-3-031-10769-6_19

Efficient Extraction of EL-Ontology Deductive Modules

Published in AAAI Conference on Artificial Intelligence (AAAI 2023), 2023

This paper explores efficient methods for extracting EL-ontology deductive modules.

Recommended citation: Yang, H., Ma, Y., & Bidoit, N. (2023). "Efficient Extraction of EL-Ontology Deductive Modules." Proceedings of the AAAI Conference on Artificial Intelligence, 37(5), 6575–6582. https://doi.org/10.1609/aaai.v37i5.25808

Efficient Computation of General Modules for ALC Ontologies

Published in International Joint Conference on Artificial Intelligence (IJCAI 2023), 2023

This paper presents a method for efficiently computing general modules for ALC ontologies.

Recommended citation: Yang, H., Koopmann, P., Ma, Y., & Bidoit, N. (2023). "Efficient Computation of General Modules for ALC Ontologies." Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI-23), 3356–3364. https://doi.org/10.24963/ijcai.2023/374

Knowledge Graph Embedding Closed under Composition

Published in Data Mining and Knowledge Discovery (DMKD), 2024

This paper proposes a KGE approach that is closed under relation composition.

Recommended citation: Zheng, Z., Zhou, B., Yang, H., Tan, Z., Sun, Z., Li, C., Waaler, A., Kharlamov, E., & Soylu, A. (2024). "Knowledge graph embedding closed under composition." Data Mining and Knowledge Discovery, pp. 1–32. https://doi.org/10.1007/s10618-024-01050-x

Alleviating Over-Smoothing via Aggregation over Compact Manifolds

Published in Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2024), 2024

This paper proposes alleviating over-smoothing in GNNs via aggregation over compact manifolds.

Recommended citation: Zhou, D., Yang, H., Xiong, B., Ma, Y., & Kharlamov, E. (2024). "Alleviating Over-Smoothing via Aggregation over Compact Manifolds." Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 390–404. https://doi.org/10.1007/978-981-97-2253-2_31

Low-Dimensional Hyperbolic Knowledge Graph Embedding for Better Extrapolation to Under-Represented Data

Published in European Semantic Web Conference (ESWC 2024), 2024

This paper presents a low-dimensional hyperbolic KGE model that better extrapolates to under-represented data.

Recommended citation: Zheng, Z., Zhou, B., Yang, H., Tan, Z., Waaler, A., Kharlamov, E., & Soylu, A. (2024). "Low-Dimensional Hyperbolic Knowledge Graph Embedding for Better Extrapolation to Under-Represented Data." European Semantic Web Conference, pp. 100–120. https://doi.org/10.1007/978-3-031-60626-7_6

Alleviating Over-Smoothing via Aggregation over Compact Manifolds (Extended Version)

Published in International Journal of Data Science and Analytics (JDSA), 2025

Extended journal version of the PAKDD 2024 paper on alleviating over-smoothing in GNNs.

Recommended citation: Zhou, D., Yang, H., Xiong, B., Ma, Y., & Kharlamov, E. (2025). "Alleviating over-smoothing via aggregation over compact manifolds (extended version)." International Journal of Data Science and Analytics, 20(8), 7055–7069. https://doi.org/10.1007/s41060-025-00726-3

TransBox: EL++-closed Ontology Embedding

Published in Proceedings of the ACM Web Conference 2025 (WWW 2025), 2025

This paper proposes TransBox, an EL++-closed ontology embedding method.

Recommended citation: Yang, H., Chen, J., & Sattler, U. (2025). "TransBox: EL++-closed Ontology Embedding." Proceedings of the ACM on Web Conference 2025, pp. 22–34. https://doi.org/10.1145/3701551.3703512

Language Models as Ontology Encoders

Published in International Semantic Web Conference (ISWC 2025), 2025

This paper explores the use of language models as encoders for OWL ontologies.

Recommended citation: Yang, H., Chen, J., He, Y., Gao, Y., & Horrocks, I. (2025). "Language Models as Ontology Encoders." International Semantic Web Conference, pp. 443–461. https://doi.org/10.1007/978-3-031-77792-9_26

RegD: Hierarchical Embeddings via Dissimilarity between Arbitrary Euclidean Regions

Published in KR 2026 (ML Track), 2026

This paper proposes RegD, a flexible Euclidean framework for hierarchical embeddings using dissimilarity between arbitrary geometric regions.

Recommended citation: Yang, H., & Chen, J. (2026). "RegD: Hierarchical Embeddings via Dissimilarity between Arbitrary Euclidean Regions." KR 2026 (ML Track). Accepted. https://arxiv.org/abs/2501.17518

Large Language Model for OWL Proofs

Published in Proceedings of the ACM Web Conference 2026 (WWW 2026), 2026

This paper studies large language models for generating and verifying OWL ontology proofs.

Recommended citation: Yang, H., Chen, J., & Sattler, U. (2026). "Large Language Model for OWL Proofs." Proceedings of the ACM Web Conference 2026. https://arxiv.org/abs/2601.12444

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015