Irreflexive and Hierarchical Relations as Translations
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Abstract (arXiv)
We consider the problem of embedding entities and relations of knowledge bases in low-dimensional vector spaces. Unlike most existing approaches, which are primarily efficient for modeling equivalence relations, our approach is designed to explicitly model irreflexive relations, such as hierarchies, by interpreting them as translations operating on the low-dimensional embeddings of the entities. Preliminary experiments show that, despite its simplicity and a smaller number of parameters than previous approaches, our approach achieves state-of-the-art performance according to standard evaluation protocols on data from WordNet and Freebase.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Apr 2013.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1.
4 papers.
- 2022Knowledge Graph Embedding: A Survey from the Perspective of Representation Spacescited by 41
- 2022Ultrahyperbolic Knowledge Graph Embeddingscited by 20
- 2021Searching to Sparsify Tensor Decomposition for N-ary Relational Datacited by 13
- 2023Knowledge Graph Completion Method Combined With Adaptive Enhanced Semantic Informationcited by 0
- 2023Knowledge Graph Completion Method Combined With Adaptive Enhanced Semantic Information4 Feb 2023
- 2022Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces7 Nov 2022
- 2022Ultrahyperbolic Knowledge Graph Embeddings1 Jun 2022
- 2021Searching to Sparsify Tensor Decomposition for N-ary Relational Data21 Apr 2021