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Exploring Research-Related Design: A Comprehensive Information System for Knowledge Production—OntoChatGPT

Kyrylo Malakhov

This chapter is part of: Kyrylo Malakhov et al. 2024. Modern Information Technologies in Scientific Research and Educational Activities

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Description
This article presents a comprehensive methodology for enhancing the capabilities of conversational AI systems, focusing on ChatGPT, through the integration of ontology-driven structured prompts and meta-learning techniques. The proposed approach, exemplified by the RDWE OntoChatGPT system, demonstrates significant potential for improving productivity and expanding knowledge bases in specific subject domains and languages. By formalizing methods for creating and configuring structured prompts and context ontologies, the system enables ChatGPT to provide specific information using selected contexts, thereby enhancing its performance in handling data across diverse linguistic and domain contexts. Through empirical testing, the study evaluates the system’s effectiveness in providing accurate responses while also highlighting areas for further refinement. The methodology’s adaptability to other conversational AI systems based on large language models underscores its potential for broader application in natural language processing and dialogue sys- tems. Future enhancements aim to refine structured prompts and incorporate ad- ditional input data to further improve system performance and accuracy. Overall, this research contributes to advancing the capabilities of conversational AI systems, paving the way for more sophisticated and effective human-computer interactions in various domains.

Description

This article presents a comprehensive methodology for enhancing the capabilities of conversational AI systems, focusing on ChatGPT, through the integration of ontology-driven structured prompts and meta-learning techniques. The proposed approach, exemplified by the RDWE OntoChatGPT system, demonstrates significant potential for improving productivity and expanding knowledge bases in specific subject domains and languages. By formalizing methods for creating and configuring structured prompts and context ontologies, the system enables ChatGPT to provide specific information using selected contexts, thereby enhancing its performance in handling data across diverse linguistic and domain contexts. Through empirical testing, the study evaluates the system’s effectiveness in providing accurate responses while also highlighting areas for further refinement. The methodology’s adaptability to other conversational AI systems based on large language models underscores its potential for broader application in natural language processing and dialogue sys- tems. Future enhancements aim to refine structured prompts and incorporate ad- ditional input data to further improve system performance and accuracy. Overall, this research contributes to advancing the capabilities of conversational AI systems, paving the way for more sophisticated and effective human-computer interactions in various domains.

Details

Published	Published By	Pages	DOI
May 1, 2024	Iowa State University Digital Press	34	10.31274/isudp.151.01
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Copyright © 2024 Kyrylo Malakhov
Citation
Malakhov K. 2024. Exploring Research-Related Design: A Comprehensive Information System for Knowledge Production—OntoChatGPT. In Kyrylo Malakhov et al. 2024. Modern Information Technologies in Scientific Research and Educational Activities