Geometric Models for (Temporally) Attributed Description Logics
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Abstract (arXiv)
In the search for knowledge graph embeddings that could capture ontological knowledge, geometric models of existential rules have been recently introduced. It has been shown that convex geometric regions capture the so-called quasi-chained rules. Attributed description logics (DL) have been defined to bridge the gap between DL languages and knowledge graphs, whose facts often come with various kinds of annotations that may need to be taken into account for reasoning. In particular, temporally attributed DLs are enriched by specific attributes whose semantics allows for some temporal reasoning. Considering that geometric models and (temporally) attributed DLs are promising tools designed for knowledge graphs, this paper investigates their compatibility, focusing on the attributed version of a Horn dialect of the DL-Lite family. We first adapt the definition of geometric models to attributed DLs and show that every satisfiable ontology has a convex geometric model. Our second contribution is a study of the impact of temporal attributes. We show that a temporally attributed DL may not have a convex geometric model in general but we can recover geometric satisfiability by imposing some restrictions on the use of the temporal attributes.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 7 of its 36 references that are in the map. First posted on arXiv 27 Aug 2021.
Builds on
7 of 36 references.
References · 7
- 2018SimplE Embedding for Link Prediction in Knowledge Graphscited by 195
- 2018Learning Sequence Encoders for Temporal Knowledge Graph Completioncited by 145
- 2020BoxE: A Box Embedding Model for Knowledge Base Completioncited by 105
- 2017Knowledge Graph Completion via Complex Tensor Factorizationcited by 80
- 2018Embedding Models for Episodic Knowledge Graphscited by 25
- 2018From Knowledge Graph Embedding to Ontology Embedding? An Analysis of the Compatibility between Vector Space Representations and Rulescited by 19
- 2015Predicting the Co-Evolution of Event and Knowledge Graphscited by 5
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Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 1.
3 papers.