Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (15): 101-108.

### Knowledge Graph Completion Method for Semantic Hierarchies of Spherical Coordinate Modeling

CHEN Heng, QI Ruihua, ZHU Yi, YANG Chen, GUO Xu, WANG Weimei

1. 1.Research Center for Language Intelligence, Dalian University of Foreign Languages, Dalian, Liaoning 116044, China
2.College of Information Science and Technology, Dalian Maritime University, Dalian, Liaoning 116026, China
• Online:2021-08-01 Published:2021-07-26

### 球坐标建模语义分层的知识图谱补全方法

1. 1.大连外国语大学 语言智能研究中心，辽宁 大连 116044
2.大连海事大学 信息科学技术学院，辽宁 大连 116026

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.