Abstract
Many knowledge graphs (KG) contain spatial and temporal information. Most KG embedding models follow triple-based representation and often neglect the simultaneous consideration of the spatial and temporal aspects. Encoding such higher dimensional knowledge necessitates the consideration of true algebraic and geometric aspects. Hypercomplex algebra provides the foundation of a well defined mathematical system among which the Dihedron algebra with its rich framework is suitable to handle multidimensional knowledge. In this paper, we propose an embedding model that uses Dihedron algebra for learning such spatial and temporal aspects. The evaluation results show that our model performs significantly better than other adapted models.
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Acknowledgement
We acknowledge the support of the following projects: SPEAKER (BMWi FKZ 01MK20011A), JOSEPH (Fraunhofer Zukunftsstiftung), the EU projects Cleopatra (GA 812997), PLATOON(GA 872592), TAILOR(EU GA 952215), CALLISTO(101004152), the BMBF projects MLwin(01IS18050) and the BMBF excellence clusters ML2R (BmBF FKZ 01 15 18038 A/B/C) and ScaDS.AI (IS18026A-F).
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Nayyeri, M. et al. (2022). Dihedron Algebraic Embeddings for Spatio-Temporal Knowledge Graph Completion. In: Groth, P., et al. The Semantic Web. ESWC 2022. Lecture Notes in Computer Science, vol 13261. Springer, Cham. https://doi.org/10.1007/978-3-031-06981-9_15
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