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.

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Publication Details

Journal
Semantic Web
Published
2025-05-01
DOI
https://doi.org/10.1177/22104968251344447
Primary Topic
Semantic Web and Ontologies
Type
article
Field-Weighted Citation Impact
0.00
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article

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

Jomar da Silva, Fernanda Baião, Kate Revoredo, Cabral Lima
Semantic Web
Semantic Web and Ontologies
article

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

Jomar da Silva, Fernanda Baião, Kate Revoredo, Cabral Lima
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.

Semantic WebVol. 16(4)
Universidade Federal do Rio de Janeiro (BR), Pontifícia Universidade Católica do Rio de Janeiro (BR), Humboldt-Universität zu Berlin (DE)
Openalex Percentile: Top 88%
Semantic Web and Ontologies
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Enhancing Ontology Matching: Lexically and Syntactically Standardizing Ontologies Through Customized Lexical Analyzers — Jomar da Silva, Fernanda Baião, et al. · Semantic Web (2025) | TGRS Research Map | TGRS