GraphMatcher: A Graph Representation Learning Approach for Ontology Matching
This addresses the interoperability problem for domain ontologies by aligning semantically similar entities, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles ontology matching by developing GraphMatcher, a system that uses graph attention to compute higher-level representations of classes and their surrounding terms, achieving remarkable results in the OAEI 2022 conference track.
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}.