Learning Entity and Relation Embeddings for Knowledge Graph Completion
Open accessAbstract
Knowledge graph completion aims to perform link prediction between entities. In this paper, we consider the approach of knowledge graph embeddings. Recently, models such as TransE and TransH build entity and relation embeddings by regarding a relation as translation from head entity to tail entity. We note that these models simply put both entities and relations within the same semantic space. In fact, an entity may have multiple aspects and various relations may focus on different aspects of entities, which makes a common space insufficient for modeling. In this paper, we propose TransR to build entity and relation embeddings in separate entity space and relation spaces. Afterwards, we learn embeddings by first projecting entities from entity space to corresponding relation space and then building translations between projected entities. In experiments, we evaluate our models on three tasks including link prediction, triple classification and relational fact extraction. Experimental results show significant and consistent improvements compared to state-of-the-art baselines including TransE and TransH.
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Other variants of translational approaches such as TransE, FTransE, STransE, TransH, and TransR also have the restrictions mentioned in Proposition 2.
“Corollary 1. Other variants of translational approaches such as TransE, FTransE, STransE, TransH [41], and TransR [22] also have the restrictions mentioned in Proposition 2.”
From SimplE Embedding for Link Prediction in Knowledge Graphs (2018) · cites this paper as [22]
The TransR section (p. 3) identifies TransR as a translational model, and the Cluster-based TransR section (p. 3) says it shares a limitation with TransE and TransH: one relation vector may not fit diverse entity pairs. Our copy does not identify the restrictions in the citing paper’s Proposition 2, so it cannot establish that TransR has those specific restrictions. (LLM, conf=0.76)
Experimental — accuracy not yet measuredTransH introduces a hyperplane, and TransR uses a relation-specific space to handle different relations, excavating more semantic information.
“TransH introduces a hyperplane, and TransR uses a relation-specific space to handle different relations, excavating more semantic information.”
From A Survey on Knowledge Graph Embeddings for Link Prediction (2021) · cites this paper as [17]
Previous research has emphasized KG completion, optimizing models across the entire spectrum of relationships within a KG.
“Previous research has emphasized KG completion, optimizing models across the entire spectrum of relationships within a KG58.”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [58]
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- Authors
- Lin, Y., Liu, Z., Sun, M., Liu, Y., & Zhu, X.
- Affiliations
- Tsinghua University (CN)
- Samsung (China) (CN)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2015-02-19
- Volume
- 29
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2184957013
- Cited by
- 3,749
- References
- 22
- Citations by year
- 2026: 135 · 2025: 369 · 2024: 518 · 2023: 534 · 2022: 467 · 2021: 504 · 2020: 397 · 2019: 341 · 2018: 245 · 2017: 142 · 2016: 69 · 2015: 27 · 2012: 1
- FWCI
- 90.92
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Funders
- National Natural Science Foundation of China (973 Program, 61202140, 61133012)
- Samsung
- National Key Research and Development Program of China (2014CB340501)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Keywords
- knowledge graph completion, knowledge graph embedding, entity embeddings, relation embeddings, TransR, link prediction, triple classification
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