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Temporal Fact Reasoning over Hyper-Relational Knowledge Graphs
Open accessAbstract
Stemming from traditional knowledge graphs (KGs), hyper-relational KGs (HKGs) provide additional key-value pairs (i.e., qualifiers) for each KG fact that help to better restrict the fact validity.In recent years, there has been an increasing interest in studying graph reasoning over HKGs.Meanwhile, as discussed in recent works that focus on temporal KGs (TKGs), world knowledge is ever-evolving, making it important to reason over temporal facts in KGs.Previous mainstream benchmark HKGs do not explicitly specify temporal information for each HKG fact.Therefore, almost all existing HKG reasoning approaches do not devise any module specifically for temporal reasoning.To better study temporal fact reasoning over HKGs, we propose a new type of data structure named hyper-relational TKG (HTKG).Every fact in an HTKG is coupled with a timestamp explicitly indicating its time validity.We develop two new benchmark HTKG datasets, i.e., Wiki-hy and YAGO-hy, and propose an HTKG reasoning model that efficiently models hyper-relational temporal facts.To support future research on this topic, we open-source our datasets and model 1 .
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Cited by 4
Metadata
- Authors
- Ding, Z., Wu, J., Wu, J., Xia, Y., Xiong, B., & Tresp, V.
- Affiliations
- University of Stuttgart (DE)
- Munich Center for Machine Learning (DE)
- Ludwig-Maximilians-Universität München (DE)
- Venue
- Findings of the Association for Computational Linguistics: EMNLP 2024
- Published
- 2024
- Pages
- 355-373
- OpenAlex
- W4404781620
- Cited by
- 4
- References
- 0
- Citations by year
- 2026: 2 · 2025: 2
- FWCI
- 0.97
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- International Max Planck Research School for Environmental, Cellular and Molecular Microbiology
- European Commission (HORIZON2020)
- Deutsche Forschungsgemeinschaft (EXC2075390740016, 471687386, EXC 2075, 390740016)
- Bundesministerium für Bildung und Forschung
- Bundesministerium für Wirtschaft und Energie (01MK20008F)
- International Max Planck Research School for Advanced Methods in Process and Systems Engineering
- Singapore Institute of Manufacturing Technology
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- hyper-relational knowledge graphs, temporal knowledge graphs, benchmark dataset
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