Leveraging Knowledge Graphs for Matching Heterogeneous Entities and Explanation

Entity matching (EM), also known as record linkage, is crucial in data integration, cleaning, and knowledge base construction. Modern matching techniques leverage deep learning and pre-trained language models (PLMs) to effectively identify matching records, showcasing significant advancements over traditional methods. However, certain critical matching aspects have received limited attention in these techniques. They heavily rely on PLMs’ encodings and face challenges in integrating external sources of knowledge to enhance matching accuracy. Additionally, these techniques often lack transparency, impeding users’ understanding of the underlying rationale for matching decisions. Furthermore, they exhibit limitations and decreased performance in handling heterogeneous records from datasets with diverse schemas. This paper presents EXKG, a novel technique that addresses these challenges and effectively matches heterogeneous records with varying attributes. EXKG combines the power of knowledge graphs (KGs) and PLMs to perform record linkage while offering explanatory insights into the matching results. We demonstrate that EXKG achieves competitive performance through experimental studies compared to state-of-the-art matching techniques. As a by-product, our solution generates explanations that give end users a comprehensive understanding of the matching process. We evaluate the quality of these explanations by using a user study and show they empower end users to make informed decisions