Link Prediction in Knowledge Hypergraph Combining Attention and Convolution Network

• Published in: Jisuanji kexue yu tansuo Vol. 17; no. 11; pp. 2734 - 2742
• Main Author: PANG Jun, XU Hao, QIN Hongchao, LIN Xiaoli, LIU Xiaoqi, WANG Guoren
• Format: Journal Article
• Language: Chinese
• Published: Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 01.11.2023
• Subjects: knowledge hypergraph; link prediction; embedding model; attention mechanism
• ISSN: 1673-9418
• DOI: 10.3778/j.issn.1673-9418.2208071

Knowledge hypergraphs (KHG) are knowledge graph of hypergraph structure. KHG link prediction aims to predict the missing relations through the known entities and relations. However, HypE, the existing optimal KHG link prediction method based on embedding model considers the location information when embedding entities, but ignores the differences in the contributions of different entities when embedding relations. And the information of the entity convolution vector is insufficient. Relation embeddings consider the entity contribution and supply the information of entity embedding, which can greatly improve the prediction ability of model. Therefore, link prediction based on attention and convolution network (LPACN) is proposed. The improved attention mechanism is applied to merging entity attention information into relation embeddings. And the number information of neighboring entities in the same tuple is integrated into the convolution network, which further supplies the information of entity convolution e