Knowledge Graph Reasoning Based on Tensor Decomposition and MHRP-Learning

Abstract

In the process of learning and reasoning knowledge graph, the existing tensor decomposition technology only considers the direct relationship between entities in knowledge graph. However, it ignores the characteristics of the graph structure of knowledge graph. To solve this problem, a knowledge graph reasoning algorithm based on multihop relational paths learning (MHRP-learning) and tensor decomposition is proposed in this paper. Firstly, MHRP-learning is adopted to obtain the relationship path between entity pairs in the knowledge graph. Then, the tensor decomposition is performed to get a novel learning framework. Finally, experiments show that the proposed method achieves advanced results, and it is applicable to knowledge graph reasoning.