Content-Aware Entity Alignment: Utilizing Structural and Semantic Similarities for Enhanced Inter-Knowledge Graph Integration
Faiqa Mehboob, Fahad Ahmed Satti, Syed Imran Ali, Muhammad Moazam Fraz · 2024
Entity alignment in knowledge graphs is critical for maximizing the utility of interconnected data, especially in domains where data interoperability is essential. Entity alignment is the process of identifying and matching equivalent entities across different knowledge graphs. Traditional entity alignment methods often treat nodes as monolithic entities, which can lead to issues such as loss of granularity, semantic heterogeneity, and inadequate semantic understanding. These challenges reduce the effectiveness of the alignment process. Our research addresses these limitations by recognizing the heterogeneous nature of node information. We propose a novel approach to entity alignment that integrates structural similarities between nodes with their semantic and content information. This methodology aims to improve precision and context awareness in entity alignment, with potential applications in various fields, including healthcare, where efficient data exchange and collaborative research are crucial.