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

Ontology matching approaches commonly leverage similarity metrics to establish mappings between entities in the ontologies participating in the process. However, the lack of standardized entity names across these ontologies can cause such metrics to overlook correct mappings. Generally, existing approaches that focus on standardizing entity names neglect the ongoing matching process, leading to inaccurate results, and fail to address the syntactic standardization of entity names. To address these issues, we introduce a novel process that standardizes entity names both lexically and syntactically through a customized lexical analyzer tailored to the ontologies participating in the process. We evaluate this process efficacy using Alin and AML , ontology matching systems, along with the Anatomy and Conference tracks of OAEI, demonstrating an improvement in matching results.

Publication Details

Journal
edoc Publication server (Humboldt University of Berlin)
Published
2025-06-17
DOI
https://doi.org/10.18452/33728
Primary Topic
Psychology Research and Bibliometrics
Type
article
Field-Weighted Citation Impact
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article

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

edoc Publication server (Humboldt University of Berlin)
Psychology Research and Bibliometrics
article

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

article en

Abstract

Ontology matching approaches commonly leverage similarity metrics to establish mappings between entities in the ontologies participating in the process. However, the lack of standardized entity names across these ontologies can cause such metrics to overlook correct mappings. Generally, existing approaches that focus on standardizing entity names neglect the ongoing matching process, leading to inaccurate results, and fail to address the syntactic standardization of entity names. To address these issues, we introduce a novel process that standardizes entity names both lexically and syntactically through a customized lexical analyzer tailored to the ontologies participating in the process. We evaluate this process efficacy using Alin and AML , ontology matching systems, along with the Anatomy and Conference tracks of OAEI, demonstrating an improvement in matching results.

edoc Publication server (Humboldt University of Berlin)
Openalex Percentile: Top 50%
Psychology Research and Bibliometrics
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