GraphMatcher: A Graph Representation Learning Approach for Ontology Matching

TL;DR

GraphMatcher is a novel ontology matching system that leverages graph attention to compute high-level class representations, significantly improving alignment accuracy in ontology interoperability tasks.

Contribution

This paper introduces GraphMatcher, a graph attention-based approach for ontology matching, demonstrating superior performance in the OAEI 2022 evaluation.

Findings

Achieved remarkable results in OAEI 2022

Utilizes graph attention for higher-level class representation

Codes are publicly available for reproducibility

Abstract

Ontology matching is defined as finding a relationship or correspondence between two or more entities in two or more ontologies. To solve the interoperability problem of the domain ontologies, semantically similar entities in these ontologies must be found and aligned before merging them. GraphMatcher, developed in this study, is an ontology matching system using a graph attention approach to compute higher-level representation of a class together with its surrounding terms. The GraphMatcher has obtained remarkable results in in the Ontology Alignment Evaluation Initiative (OAEI) 2022 conference track. Its codes are available at ~\url{https://github.com/sefeoglu/gat_ontology_matching}.