Knowledge Graph Completion Method Fusing Entity Descriptions and Topological Structure

Knowledge graph completion aims to predict missing entities and relationships in given triplets to enhance the completeness and quality of the knowledge graph.Existing knowledge graph completion methods typically only consider the structural information of triplets or the individual additional information of entities,such as textual descriptions or topological structure information.This overlooks the fusion of multiple types of additional information to enhance entity feature information,leading to suboptimal performance in completing missing entities.To address this issue,this paper proposes a knowledge graph completion method integrating entity text descriptions and topological structure information,referred to as FuDS-KGC,to enhance the performance of knowledge graph completion tasks.This method first extracts relationship-specific feature representations from entity textual descriptions using Transformer and attention mechanisms to enhance the representation feature information of entity descriptions.Next,it constructs first-order neighbor subgraphs for entities and obtains topological structure features through a graph attention network.Finally,a dynamic gated fusion mechanism is designed to integrate entity textual descriptions and topo-logical structure features to enhance the comprehensive feature representation of entities and overcoming the limitation of existing research focusing on the fusion of singular additional information.Experimental results on FB15k-237 and WN18RR datasets demonstrate the effectiveness of FuDS-KGC.