Enhancing Ontology Matching: Lexically and Syntactically Standardizing Ontologies Through Customized Lexical Analyzers

Ontology matching systems commonly leverage linguistic metrics to establish mappings between entities within the ontologies undergoing alignment. However, due to the absence of standardized entity names across these ontologies, such metrics may lead to some correct mappings not to be selected. Existing methodologies, which focus on standardizing entity names, often do so without considering the ongoing matching, potentially resulting in inaccurate outcomes. These tools also, in general, are not concerned with the syntactic standardization of entity names. To address this issue, in this paper, we introduce a novel approach to standardize both lexically and syntactically the entity names through the development of a customized lexical analyzer tailored to the aligned ontologies. We evaluate the efficacy of this approach using ALIN, an interactive ontology matching system, along with the human and mouse ontologies from the Anatomy track of the OAEI. Our findings demonstrate an improvement in the quality of the alignment results.