Generative artificial intelligence analysis for the elaboration of a conceptual definition: a comparative study using Scite and Perplexity
DOI:
https://doi.org/10.47989/ir31263060Keywords:
Generative Artificial Intelligence, Conceptual Definition, Hepatic InjuriesAbstract
Introduction. This study investigates the use of Scite and Perplexity research tools to define concepts for building an ontology in the domain of hepatic injuries.
Method. A comparative study approach was adopted, focusing on the definition of the term hepatic tissue. The tools were studied in five steps, including analysis of information needs, tool selection, search planning, analysis, and use of the synthesised information.
Analysis. The generated AI content was analysed and then validated by a domain expert. The definitions generated were compared, considering the elements that constitute a conceptual definition. The syntheses were examined according to their sections, and the content was compared in terms of meaning.
Results. Scite presented more colloquial and concise language; Perplexity presented more technical and detailed text. Both tools addressed the genus, composition, and function of hepatic tissue, but only Perplexity included hepatic injuries. The differences between genus assigned to the tissue revealed distinct approaches: functional (Scite) versus anatomical (Perplexity). The explanations delivered enabled the development of the conceptual definition of hepatic tissue.
Conclusion. Well-designed prompts generate more accurate conceptual syntheses. Domain experts´ collaboration and Knowledge Organisation theories are essential to ensure consistency and accuracy in the development of conceptual definitions for ontology building.
References
Adamopoulou, E., & Moussiades, L. (2020). An overview of chatbot technology. Artificial Intelligence Applications and Innovations, 584, 373-383, 2020. DOI:10.1007/978-3-030-49186-4_31
Alpaydin, E. (2010). Introduction to Machine Learning. (2ª ed.). MIT Press.
Barité, M. (2018). Literary warrant. Knowledge Organization, 45(6), 517–536. https://doi.org/10.5771/0943-7444-2018-6-517
Barr, A., & Feigenbaum, E. A. (1981). The Handbook of Artificial Intelligence. HeurisTech Press.
Biagetti, M. T. (2021). Ontologies as knowledge organization systems. Knowledge Organization, 48(2), 152–176. https://doi.org/10.5771/0943-7444-2021-2-152
Brody, S. (2021). Scite. Journal of the Medical Library Association, 109(4), 707-709. http://dx.doi.org/10.5195/jmla.2021.1331
Brookshear, J. G. (2008). Ciência da Computação - uma visão abrangente. (7ª ed.). Bookman.
Dahlberg, I. (1978). A referent-oriented analytical concept theory for Interconcept. International Classification, 5(3), 142–150. https://doi.org/10.5771/0943-7444-1978-3-142
Dahlberg, I. (1981). Les objets, les notions, les définitions et ses formes. In G. Rondeau & H. Felber (Eds.), Textes choisis de terminologie. GIRSTERM.
Dahlberg, I. (1992). Knowledge organization and terminology: Philosophical and linguistic bases. International Classification, 19(2), 65–71. https://www.imrpress.com/journal/ko/19/2/10.5771/0943-7444-1992-2-65
Dahlberg, I. (2006). Knowledge organization: A new science? Knowledge Organization, 33(1), 11-19. https://doi.org/10.5771/0943-7444-2006-1-11
Franco, C. R. (2017). Inteligência Artificial. Uniasselvi.
Grudin, J. A. (2011). Moving Target: The Evolution of HCI. In J Jacko (Ed.). The Human-Computer Interaction Handbook. (3ª ed.). Taylor & Francis.
Junqueira, L. C., & Carneiro, J. (2023). Histologia básica – texto e atlas (14ª ed.). Guanabara Koogan.
Nedobity, W. (1985). Terminology and artificial intelligence. Knowledge Organization, 12(1), 17–19.
Oliveira, R. F. (2018). Inteligência Artificial. Editora e Distribuidora Educacional S.
San Martín, A. (2025). What generative artificial intelligence means for terminological definitions. arXiv. https://arxiv.org/pdf/2402.16139
Saracevic, T. (1996). Ciência da informação: origem, evolução e relações. Perspectivas em Ciência da Informação, 1(1), 41-62. https://periodicos.ufmg.br/index.php/pci/article/view/22308
Seppälä, S., Ruttenberg, A., & Smith, B. (2017). Guidelines for writing definitions in ontologies. Ciência da Informação (Brasília), 46(1), 73–88. https://doi.org/10.18225/ci.inf.v46i1.4015
Seppälä, S., Ruttenberg, A., Schreiber, Y., & Smith, B. (2016). Definitions in ontologies. Cahiers de Lexicologie, 109(2), 175–207. https://classiques-garnier.com/cahiers-de-lexicologie-2016-2-n-109-la-definition-definitions-in-ontologies-en.html
Silva Neto, V. J.; Bonacelli, M. B. M.; Pacheco, C. A. (2020). O sistema tecnológico digital: inteligência artificial, computação em nuvem e Big Data. Revista Brasileira de Inovação, 19, DOI: https://doi.org/10.20396/rbi.v19i0.8658756
Trindade, A. S. C. E., & Oliveira, H. P. C. (2024). Inteligência artificial (IA) generativa e competência em informação: Habilidades informacionais necessárias ao uso de ferramentas de IA generativa em demandas informacionais de natureza acadêmica-científica. Perspectivas em Ciência da Informação, 29, e47485. https://periodicos.ufmg.br/index.php/pci/article/view/47485
Vidal Sabanés, L., & Da Cunha, I. (2025). AI as a resource for the clarification of medical terminology: An analysis of its advantages and limitations. Terminology, 31(1), 37–71.
Zachary, J. F. (2018). Bases da patologia em veterinária (6ª ed.). Elsevier.
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Copyright (c) 2026 Jóice Cleide Cardoso Ennes de Souza, Rosana Portugal Tavares de Moraes, Elan Cardozo Paes de Almeida, Matheus Souza da Silva, Sergio Castro Martins, Fernando Henrique Silva Lisot
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