Hybrid approach for accurate and interpretable representation learning of knowledge graph

Abstract

Representation learning of knowledge graph aims to embed both entities and relations into a low-dimensional space. However, there are still some gaps in the knowledge graph embedding methods in providing interpretation of knowledge graph while encoding the semantic meaning of the concepts and structured information of knowledge graphs. To address this issue, we propose a hybrid approach for Accurate and Interpretable Representation Learning (AIRL) method for embedding entities and relations of knowledge graphs by utilizing the rich information located in entity descriptions and hierarchical types of entities. Here we use hybrid approach to learn interpretable knowledge representations by capturing the semantics and structure of entities using this rich information. We adopt FB15K dataset generated from a large knowledge graph freebase, to evaluate the performance of the proposed model. The results of experiments demonstrate AIRL significantly outperforms translation embeddings and other state-of-the-art methods.