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

Submitted by Jomar Silva on 09/29/2024 - 19:39

Tracking #: 3763-4977

Link to the paper as published by IOS Press: https://journals.sagepub.com/doi/epub/10.1177/22104968251344447

Authors: 
Jomar Silva
Kate Revoredo
Fernanda Araujo Baião
Cabral Lima

Responsible editor: 
Guest Editors OM-ML 2024

Submission type: 
Full Paper
Abstract: 
Ontology matching systems 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 methodologies 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 approach that standardizes entity names both lexically and syntactically through a customized lexical analyzer tailored to the ontologies participating in the process. We evaluate this approach's efficacy using Alin and AML, ontology matching systems, along with the Anatomy and Conference tracks of OAEI, demonstrating an improvement in matching results.
Full PDF Version: 
PDF icon swj3763.pdf
Previous Version: 
Enhancing Ontology Matching: Lexically and Syntactically Standardizing Ontologies Through Customized Lexical Analyzers
Tags: 
Reviewed
Long-term Stable Link to Resources: 
https://osf.io/gc6jm/

Decision/Status: 
Accept

Solicited Reviews:

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Review #1
Anonymous submitted on 08/Oct/2024
Suggestion:
Accept
Review Comment:

After reviewing the updated manuscript, I find the changes satisfactory, and I now recommend the paper for publication.

Review #2
Anonymous submitted on 09/Jan/2025
Suggestion:
Accept
Review Comment:

In my initial review, I had concerns around "(2) significance of the results", "(3) quality of writing", and relevance for the special issue. The authors have addressed all the issues in this revision, and the paper is now a strong contribution to this field.

The only remaining issue is that "ChatGPT" is used without proper citation and incorrectly defined as a model. ChatGPT is a software, and you have a choice of several LLMs for the software. You need to specify which model you have used. Is it gpt-4o? I would say the model name should also be a parameter defined in parameters.txt to run the code.