# TransA: An Adaptive Approach for Knowledge Graph Embedding

arXiv: https://arxiv.org/abs/1509.05490 · HTML: https://mlsanity.com/paper/1509.05490/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2015.

## Built on by (most cited first, 19 of 19)

- [Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces](https://mlsanity.com/paper/2211.03536/) (2022)
- [HousE: Knowledge Graph Embedding with Householder Parameterization](https://mlsanity.com/paper/2202.07919/) (2022)
- [Knowledge Graph Self-Supervised Rationalization for Recommendation](https://mlsanity.com/paper/2307.02759/) (2023)
- [Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embedding](https://mlsanity.com/paper/2110.14450/) (2021)
- [GreenKGC: A Lightweight Knowledge Graph Completion Method](https://mlsanity.com/paper/2208.09137/) (2022)
- [Negative Sampling in Knowledge Graph Representation Learning: A Review](https://mlsanity.com/paper/2402.19195/) (2024)
- [A Comprehensive Survey on Self-Supervised Learning for Recommendation](https://mlsanity.com/paper/2404.03354/) (2024)
- [Ranking vs. Classifying: Measuring Knowledge Base Completion Quality](https://mlsanity.com/paper/2102.06145/) (2021)
- [Knowledge Graph Embeddings: A Comprehensive Survey on Capturing Relation Properties](https://mlsanity.com/paper/2410.14733/) (2024)
- [Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods](https://mlsanity.com/paper/2506.05626/) (2025)
- [A Probit Tensor Factorization Model For Relational Learning](https://mlsanity.com/paper/2111.03943/) (2021)
- [Retrieval of Scientific and Technological Resources for Experts and Scholars](https://mlsanity.com/paper/2204.06142/) (2022)
- [Knowledgebra: An Algebraic Learning Framework for Knowledge Graph](https://mlsanity.com/paper/2204.07328/) (2022)
- [SubGraph Networks based Entity Alignment for Cross-lingual Knowledge Graph](https://mlsanity.com/paper/2205.03557/) (2022)
- [Knowledge Graph Completion Method Combined With Adaptive Enhanced Semantic Information](https://mlsanity.com/paper/2302.02116/) (2023)
- [Location Sensitive Embedding for Knowledge Graph Reasoning](https://mlsanity.com/paper/2401.10893/) (2023)
- [SparseTransX: Efficient Training of Translation-Based Knowledge Graph Embeddings Using Sparse Matrix Operations](https://mlsanity.com/paper/2502.16949/) (2025)
- [A Survey of Task-Oriented Knowledge Graph Reasoning: Status, Applications, and Prospects](https://mlsanity.com/paper/2506.11012/) (2025)
- [Logical Expressivity and Explanations for Monotonic GNNs with Scoring Functions](https://mlsanity.com/paper/2508.14091/) (2025)

## Abstract (arXiv)

Knowledge representation is a major topic in AI, and many studies attempt to represent entities and relations of knowledge base in a continuous vector space. Among these attempts, translation-based methods build entity and relation vectors by minimizing the translation loss from a head entity to a tail one. In spite of the success of these methods, translation-based methods also suffer from the oversimplified loss metric, and are not competitive enough to model various and complex entities/relations in knowledge bases. To address this issue, we propose TransA, an adaptive metric approach for embedding, utilizing the metric learning ideas to provide a more flexible embedding method. Experiments are conducted on the benchmark datasets and our proposed method makes significant and consistent improvements over the state-of-the-art baselines.