Title: Path embedded hybrid reasoning model for relation prediction in knowledge graphs
Authors: Danyang Zhao; Xinzhi Wang; Xiangfeng Luo
Addresses: School of Computer Engineering and Science, Shanghai University, Shanghai, China ' School of Computer Engineering and Science, Shanghai University, Shanghai, China ' School of Computer Engineering and Science, Shanghai University, Shanghai, China
Abstract: To conquer incompleteness of knowledge graphs (KGs), we focus on a relation prediction task that completes KGs by ranking all relationships between entities and selecting the top one. Most existing methods for relation prediction are representation-based models, which learn the structural information of KGs to obtain the embeddings of entities and relationships but fail to effectively use relation paths to predict potential relationships between two entities. The traditional path-based logical models consider the path characteristics but ignore the global semantics of entities in KGs, such as surrounding entities and relationships. In this paper, we propose path embedded hybrid reasoning model (PE-HRM), a novel reasoning mode based on logical reasoning, representation learning and neural network. PE-HRM considers structural characteristics of entities and path features between them and effectively fuses these features. Finally, we evaluate PE-HRM on datasets FB15K237 and NELL995. Experimental results show that PE-HRM significantly outperforms baseline models.
Keywords: knowledge graphs; relation prediction; hybrid reasoning.
International Journal of Embedded Systems, 2022 Vol.15 No.1, pp.44 - 52
Received: 02 Mar 2021
Accepted: 16 Mar 2021
Published online: 08 Apr 2022 *