Multi-dimensional approach to knowledge graph completion: integrating entity types, relations, and paths
Year: 2025
Type: conference-paper
Abstract: Knowledge graphs (KGs), as highly structured information storage systems, play a crucial role in various fields. However, existing knowledge graphs still face the issue of incomplete knowledge, which limits their potential in practical applications. To address this problem, this paper proposes A novel Multi-Dimensional Knowledge Graph Completion method (AMD-KGC) that integrates entity types and topology-aware information. This method significantly improves the accuracy and generalization ability of knowledge graph completion by integrating entity type information, relation context information, and relation path information. Specifically, AMD-KGC learns from contextual information, uses an attention mechanism to adaptively calculate entity type weights, and introduces entity path information to further enrich the representation of entity pairs. Experimental results demonstrate that on the FB15k-237, WN18RR, and EDUDB (Educational Database Engine) datasets, AMD-KGC improves key performance metrics MRR (Mean Reciprocal Rank) and Hits@10 by 9.2% and 10.5%, respectively, compared to current leading technologies, demonstrating its superiority in knowledge graph completion tasks.
DOI: 10.1117/12.3061788
Authors: Shenyi Qian, Yilong Fu, Kunlin Li, daiyi Li, Yifan Sun
Institution: Zhengzhou University of Light Industry
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409784716