Full Triple Matcher: Integrating all triple elements between heterogeneous Knowledge Graphs

TL;DR

This paper introduces a comprehensive triple matching method for integrating heterogeneous knowledge graphs by combining label and triple matching techniques, addressing the challenge of diverse contextual information.

Contribution

It presents a novel triple matching approach that enhances knowledge graph integration, especially in complex and diverse contexts, and introduces a new dataset for evaluation.

Findings

Achieves high accuracy in diverse test cases

Demonstrates competitive performance in OAEI competition

Provides a new dataset for triple matching evaluation

Abstract

Knowledge graphs (KGs) are powerful tools for representing and reasoning over structured information. Their main components include schema, identity, and context. While schema and identity matching are well-established in ontology and entity matching research, context matching remains largely unexplored. This is particularly important because real-world KGs often vary significantly in source, size, and information density - factors not typically represented in the datasets on which current entity matching methods are evaluated. As a result, existing approaches may fall short in scenarios where diverse and complex contexts need to be integrated. To address this gap, we propose a novel KG integration method consisting of label matching and triple matching. We use string manipulation, fuzzy matching, and vector similarity techniques to align entity and predicate labels. Next, we identify mappings between triples that convey comparable information, using these mappings to improve entity-matching accuracy. Our approach demonstrates competitive performance compared to leading systems in the OAEI competition and against supervised methods, achieving high accuracy across diverse test cases. Additionally, we introduce a new dataset derived from the benchmark dataset to evaluate the triple-matching step more comprehensively.