Knowledge Graph Embedding by Translating on Hyperplanes
Open accessAbstract
We deal with embedding a large scale knowledge graph composed of entities and relations into a continuous vector space. TransE is a promising method proposed recently, which is very efficient while achieving state-of-the-art predictive performance. We discuss some mapping properties of relations which should be considered in embedding, such as reflexive, one-to-many, many-to-one, and many-to-many. We note that TransE does not do well in dealing with these properties. Some complex models are capable of preserving these mapping properties but sacrifice efficiency in the process. To make a good trade-off between model capacity and efficiency, in this paper we propose TransH which models a relation as a hyperplane together with a translation operation on it. In this way, we can well preserve the above mapping properties of relations with almost the same model complexity of TransE. Additionally, as a practical knowledge graph is often far from completed, how to construct negative examples to reduce false negative labels in training is very important. Utilizing the one-to-many/many-to-one mapping property of a relation, we propose a simple trick to reduce the possibility of false negative labeling. We conduct extensive experiments on link prediction, triplet classification and fact extraction on benchmark datasets like WordNet and Freebase. Experiments show TransH delivers significant improvements over TransE on predictive accuracy with comparable capability to scale up.
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TransH 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 [15]
The TransH section (p. 3) confirms that the paper introduces relation-specific hyperplanes and projects entity embeddings onto them to represent different roles across relations. The cited paper does not address TransR or its use of a relation-specific space, so it supports only the TransH part of the sentence. (LLM, conf=0.90)
Experimental — accuracy not yet measuredMany existing translational approaches are not fully expressive.
“We prove that many existing translational approaches (see e.g., [4, 17, 41, 26]) are not fully expressive and we identify severe restrictions on what they can represent.”
From SimplE Embedding for Link Prediction in Knowledge Graphs (2018) · cites this paper as [41]
The section “Relations’ Mapping Properties in Embedding” (p. 2) identifies representational restrictions of TransE for reflexive and one-to-many/many-to-one relations. This supports the general concern about limited expressiveness, but the cited paper analyzes TransE specifically; it does not establish that many translational approaches are not fully expressive. (LLM, conf=0.85)
Experimental — accuracy not yet measuredTransX can infer and model the symmetry/antisymmetry pattern when g_r,1 = g_r,2, e.g. in TransH.
“TransX can infer and model the symmetry/antisymmetry pattern when g r, 1 = g r, 2 , e.g. in TransH (Wang et al., 2014)”
From RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space (2019) · cites this paper as Wang et al., 2014
The TransH section (p. 3) says that head and tail entities are projected onto the same relation-specific hyperplane, consistent with the claim’s use of TransH as an example of shared projections. But our copy does not state that TransH infers or models symmetry and antisymmetry patterns, or use the notation g_r,1 = g_r,2; its discussion of relation properties concerns reflexive and one-to-many/many-to-one/many-to-many relations. (LLM, conf=0.83)
Experimental — accuracy not yet measuredOther 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 [41]
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- Authors
- Wang, Z., Zhang, J., Feng, J., & Chen, Z.
- Affiliations
- Sun Yat-sen University (CN)
- Microsoft Research Asia (China) (CN)
- Microsoft (United States) (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2014-06-21
- Volume
- 28
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2283196293
- Cited by
- 3,915
- References
- 3
- Citations by year
- 2026: 174 · 2025: 421 · 2024: 550 · 2023: 616 · 2022: 491 · 2021: 504 · 2020: 376 · 2019: 331 · 2018: 215 · 2017: 126 · 2016: 72 · 2015: 36 · 2014: 2 · 2012: 1
- FWCI
- 46.33
- 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
- Microsoft Research
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- knowledge graph embedding, TransH, TransE, reflexive relations, link prediction, fact extraction, model efficiency
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