Abstract
Geometric aspects of knowledge graph embedding models directly impact their capability to preserve knowledge from the original graph to the vector space. For example, the capability to preserve structural patterns such as hierarchies, loops, and paths present as relational structures in a knowledge graph depends on the underlying geometry. In these years, temporal information has gained lots of attention from researchers. While non-Euclidean geometry, e.g. Hyperbolic Geometry, has been shown to work well in static knowledge graph embedding models for such relational structures, this does not hold for temporal information in knowledge graphs. This is due to the different characteristics of temporal information: time can be seen mostly as a linear construct and using a geometry that is not suitable for this can adversely affect performance. To address this research gap, we provide a novel temporal knowledge graph embedding model that combines different geometries: the non-temporal part of the knowledge is mapped to a hyperbolic space and the temporal part is mapped to a Euclidean space. Our extensive evaluations on several benchmark datasets show a significant performance improvement in comparison to state-of-the-art models.
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Acknowledgement
We acknowledge the support of the China Scholarship Council for the first author, and contribution of the following EU projects: CALLISTO(101004152), E-Vita (101016453), ScaDS.AI (IS18026A-F). We thank the Natural Science Foundation of China (42271391 and 62006214), Joint Funds of Equipment Pre-Research and Ministry of Education of China Grant No. 8091B022148, the 14th Five-year Pre-research Project of Civil Aerospace in China, and Hubei excellent young and middle-aged science and technology innovation team plan project under Grant No. T2021031. The authors are grateful to the Center for Information Services and High Performance Computing [Zentrum für Informationsdienste und Hochleistungsrechnen (ZIH)] at TU Dresden for providing its facilities for high throughput calculations, and Leipzig universities.
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Song, B., Xu, C., Amouzouvi, K., Wang, M., Lehmann, J., Vahdati, S. (2023). Distinct Geometrical Representations for Temporal and Relational Structures in Knowledge Graphs. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14171. Springer, Cham. https://doi.org/10.1007/978-3-031-43418-1_36
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