A knowledge graphs representation method based on IsA relation modeling

Abstract

Knowledge graph representation learning, which is able to support further knowledge computation and reasoning, has drawn much research attention in the field of artificial intelligence in recent years. Due to data sparsity, traditional knowledge graph methods focusing on the observed facts have poor performance. Therefore, researchers introduce auxiliary information to tackle this issue. As a kind of prior knowledge containing rich semantics, IsA relation could be used to boost model performance. We propose a knowledge representation learning method combined with isA relation modeling. Specifically, we model two features of isA relation—transitivity and antisymmetry. The transitivity is modeled by the projection invariance of vectors, and antisymmetry is modeled by partial order constraints. We use RotatE as our base model which could also be replaced by other knowledge embedding model with higher dimension. Finally, experimental results of link prediction and triple classification task on two datasets verify the proposed model’s effectiveness outperforming baseline models.