关键词: 知识图谱, 语义分层, 知识图谱补全, 球坐标系, 链接预测

Abstract:

The knowledge graph is a structured representation of real-world triples. Typically, triples are represented as （head entity, relationship, tail entity）, which means that the head entity and tail entity are related to each other through a specific relationship. Aiming at the problem of sparse data widely existing in the knowledge graph, this paper proposes a knowledge graph completion method for semantic hierarchies of spherical coordinate modeling. In this paper, the spherical coordinate system is used to model entities and relationships for link prediction. Specifically, the radial coordinate aims to model entities at different levels, and the entity with a smaller radius has a higher level; the angular coordinate aims to distinguish entities at the same level, that is, entities with the same length and different angles. This method maps the entities into the spherical coordinate system, which can effectively model the semantic hierarchies that are common in the knowledge graph. In the experiment, the public data sets WN18RR, FB15K-237 and YAGO3-10 are used to carry out related link prediction experiments. Experimental results show that in WN18RR, the Mean Reciprocal Rank is 3.6% higher than RotatE, and Hit@10 is 1.9% higher than RotatE. In FB15K-237, the Mean Reciprocal Rank is improved 4.8% than ConvKB, Hit@10 is 3.5% higher than ConvKB. Experiments show that the knowledge graph completion method of spherical coordinate modeling semantic hierarchies can effectively improve the accuracy of triple prediction.

Key words: knowledge graph, semantic hierarchies, knowledge graph completion, spherical coordinate system, link prediction