TransA: An Adaptive Approach for Knowledge Graph Embedding

Understand

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 \textbf{TransA}, an adaptive metric approach for embedding, utilizing the metric learning ideas to provide a more flexible embedding method.