Leveraging LLMs for Collaborative Ontology Engineering in Parkinson Disease Monitoring and alerting

Tracking #: 771-1761

Flag : Reject (Pre-Screening)

Authors: 

Georgios Bouchouras
Dimitrios Doumanas
Andreas Soularidis
Konstantinos Kotis
George A. Vouros

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Submission Type: 

Article in Special Issue (note in cover letter)

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Cover Letter: 

Dear editor, I am writing to submit our paper titled "Leveraging LLMs for Collaborative Ontology Engineering in Parkinson Disease Monitoring and Alerting" for consideration in the special issue of "Knowledge Graphs and Neurosymbolic AI." We received an invitation from the organizers of the GeNeSy 2024 workshop to contribute this extended version. This paper extends our previous work (presented at the GeNeSy 2024 workshop) by introducing a new methodology (SimX-HCOME) for LLM-enhanced ontology engineering, utilizing the Claude LLM, adding LLM-based SWRL rule generation capability, and conducting a comparison of the highest LLM performance and the degree of human involvement across all methods. We believe this extended version provides significant advancements and insights that are highly relevant to the special issue's theme. Thank you for considering our submission. We look forward to the possibility of contributing to your esteemed journal. Sincerely, Dr. Georgios Bouchouras cti23010@ct.aegean.gr bouhouras@yahoo.com

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Approved

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  • Reviewed

Decision: 

Reject (Pre-Screening)