Knowledge Graph Completion Model Based on Entity and Relation Fusion

[Objective] This study aggregates the global information of knowledge graph through a weighted graph convolutional neural network and a relational induction mechanism, aiming to enhance the quality of the knowledge graph representation and completion. [Methods] We proposed an end-to-end learning model for the knowledge graph completion task, which included a neighborhood information aggregation module, an entity relationship fusion module, an interaction module, as well as a prediction module. This new model aggregates the neighborhood information of entities to enrich their representations. It also enhances the interaction between entities and relationship representations with a core tensor. [Results] We examined the new model with the FB15K237, WN18RR, Kinship, and UMLS datasets. Compared with traditional knowledge graph completion models, the Hits@1 indicators of the proposed model increased by 4.1%, 3.9%, 17.8%, and 5.3% on the four datasets, respectively. [Limitations] We did not explore the performance of our new model on information retrieval and recommendation systems. [Conclusions] The proposed model significantly improves the effectiveness of the knowledge graph completion, which helps us identify missing information in knowledge graphs and may benefit information retrieval and automatic Q&A applications. © 2023, Chinese Academy of Sciences. All rights reserved.