ARTIFICIAL INTELLIGENCE AND THE UNIVERSE OF KNOWLEDGE
DOI:
https://doi.org/10.11603/mie.1996-1960.2025.1-2.15985Keywords:
artificial intelligence, universe of knowledge, knowledge organization, ontologies, noosphere, explainable artificial intelligence, large language models, semantic technologiesAbstract
Background. The rapid expansion of digital information, cloud computing, and global communication networks has significantly transformed the processes of knowledge creation, storage, and dissemination. In this context, artificial intelligence plays an increasingly important role in structuring and interpreting large volumes of heterogeneous knowledge. The concept of the universe of knowledge provides a theoretical framework for organizing and integrating evolving knowledge structures.
Materials and Methods. The study is based on theoretical and conceptual analysis of contemporary research on knowledge organization, ontology engineering, and artificial intelligence. Methods of comparative analysis and interdisciplinary synthesis were applied to examine the role of ontological models in structuring knowledge and improving the interpretability of artificial intelligence systems.
Results. The analysis demonstrates that continuous accumulation and restructuring of information may lead to the loss of semantic coherence within knowledge systems. The concept of the universe of knowledge provides a systemic framework for integrating diverse knowledge domains and preserving conceptual continuity. Ontological models enable explicit representation of domain concepts, properties, and relationships, which improves semantic interoperability and supports reasoning processes in artificial intelligence systems. The integration of ontologies with large language models enhances transparency, interpretability, and contextual relevance of AI-generated outputs, particularly in knowledge-intensive fields such as medicine.
Conclusions. Ontologies represent a key methodological instrument for structuring knowledge and improving the reliability of artificial intelligence systems. The integration of ontological knowledge with large language models supports the development of explainable and responsible AI technologies.
Further progress in this field requires interdisciplinary collaboration and the development of global strategies for the preservation, governance, and ethical use of human knowledge in the digital era.
References
Dahlberg, I. (1995). Current trends in knowledge organization. In F. J. Garcia Marco (Ed.), Organización del conocimiento en sistemas de información y documentación (pp. 7–25). Zaragoza: Universidad de Zaragoza.
Gill, S. S., Xu, M., Ottaviani, C., Patros, P., Bahsoon, R., et al. (2022). AI for next generation computing: Emerging trends and future directions. Internet of Things, 19, 100514. https://doi.org/10.1016/j.iot.2022.100514
Yu, H. (2024). Why do people use Metaverse? A uses and gratification theory perspective. Telematics and Informatics, 89, 102110. https://doi.org/10.1016/j.tele.2024.102110
Fauseweh, B. (2024). Quantum many-body simulations on digital quantum computers: State-of- the-art and future challenges. Nature Communications, 15(1), 2123. https://doi.org/10.1038/s41467-024-46402-9
Satija, M. P., Martínez-Ávila, D. (2017). Mapping of the universe of knowledge in different classification schemes. International Journal of Knowledge Content Development & Technology, 7(2), 85–105.
Sen, B. K. (2009). Universe of knowledge from a new angle. Annals of Library and Information Studies, 56, 7–12.
Smith, B. (2003). The Blackwell guide to the philosophy of computing and information. In L. Floridi (Ed.), The Blackwell guide to the philosophy of computing and information. Oxford: Blackwell Publishing.
Guizzardi, G. (2006). On ontology, ontologies, conceptualizations, modeling languages, and (meta) models. In Databases and Information Systems IV: Selected Papers from the Seventh International Baltic Conference DB&IS 2006. Vilnius.
Tewari, A. (2014). Medical ontology: Big data big challenges.
Mintser, O. P., Popova, M. A., Prykhodniuk, V. V., Stryzhak, O. Ye. (2021). Ontolohiia v systemnii biomedytsyni. Kyiv. 300 p.
Herfel, W. (2000). Marc De Mey, The Cognitive Paradigm: An Integrated Understanding of Scientific Development, reprint, with a new introduction. Minds and Machines, 10(1), 165–168. https://doi.org/10.1023/A:1008349816805.
Dahlberg, I. (2006). Knowledge organization: A new science? Knowledge Organization, 33(1), 11–19.
Dahlberg, I. (2010). International Society for Knowledge Organization (ISKO). In M. J. Bates & M. N. Maack (Eds.), Encyclopedia of Library and Information Sciences (Vol. 4, pp. 2941–2949). Boca Raton: CRC Press.
Dahlberg, I. (2014). What is knowledge organization? Knowledge Organization, 41(1), 85–91.
Slavic, A. (2022). Competencies and skills for subject analysis and access. In Knowledge Organization ompetencies and Skills Webinar. The Hague: IFLA.
Mey, M. de. (1992). The cognitive paradigm: An integrated understanding of scientific development. Chicago: University of Chicago Press.
Hjørland, B. (2008). What is knowledge organization? Knowledge Organization, 35(2–3), 86–101.
Jacob, E. K., Shaw, D. (1998). Sociocognitive perspectives on representation. Annual Review of Information Science and Technology, 33, 131–185.
Aristóteles. (2001). Ética a Nicômaco. Brasília: Universidade de Brasília.
Aristóteles. (1984). Metafísica. São Paulo: Editora Abril.
Rother, E. T. (2007). Revisão sistemática x revisão narrativa. Acta Paulista de Enfermagem, 20(2), 5-6.
Rosch, E. (1978). Principles of categorization. In E. Rosch & B. Lloyd (Eds.), Cognition and Categorization (pp. 27–48). Hillsdale: Lawrence Erlbaum Associates.
Ranganathan, S. R. (1967). Prolegomena to library classification. London: Asia Publishing House.
Maculan, B. C. M. dos S. (2014). Taxonomia facetada e navegacional: busca e recuperação na BDTD. Curitiba: Appris.
Jacob, E. K. (2004). Classification and categorization: A difference that makes a difference. Library Trends, 52(3), 515–540.
Gruber, T. R. (1995). Toward principles for the design of ontologies used for knowledge sharing. International Journal of Human-Computer Studies, 43(5–6), 907–928. https://doi.org/10.1006/ijhc.1995.1081.
Wu, X.-K., Deng, K.-Q., Zhao, T.-F., Chen, W.-N. (2024). Collective intelligence for preventing pandemic crises: A model-centralized organizational framework. IEEE Systems, Man and Cybernetics Magazine, 10(3), 31–43. https://doi.org/10.1109/MSMC.2024.3352850
Lehoux, P., Rivard, L., de Oliveira, R. R., Mörch, C. M., Alami, H. (2023). Tools to foster responsibility in digital solutions that operate with or without artificial intelligence: A scoping review for health and innovation policymakers. International Journal of Medical Informatics, 170, 104933.
Downloads
Published
How to Cite
Issue
Section
License
Journal Medical Informatics and Engineering allows the author(s) to hold the copyright without registration
The majority of Medical Informatics and Engineering Open Access journals publish open access articles under the terms of the Creative Commons Attribution (CC BY) License which permits use, distribution and reproduction in any medium, provided the original work is properly cited. The remaining journals offer a choice of licenses.
This journal is available through Creative Commons (CC) License CC-BY 4.0
