Automatic extraction of tacit knowledge in a digital library: using artificial intelligence mechanisms
DOI:
https://doi.org/10.11606/issn.2178-2075.incid.2025.231405Keywords:
Digital Library, Artificial Intelligence, Machine Learning, Text Mining, Clustering, Knowledge GraphsAbstract
Objective: This study reports the results of applied research to build intelligent-agent solutions based on natural language processing, text mining, and machine learning, along with visual data presentations (graphs and lists), to extract and display tacit knowledge contained in scientific articles within a digital library database. Methodology: The study is qualitative and interpretive, arising from interactions among researchers and the analysis of extracted data results. Methodologically, it is grounded in Design Science Research, as this approach is metatheoretical. Epistemologically, the study is based on Design Science, which generates knowledge by examining how science-and-technology research designs artifacts to solve problems. The proof of concept—using AI techniques to extract knowledge from data in a digital library of scientific articles—constitutes applied, domain-based research. Results: Searches conducted by computational intelligent agents yielded satisfactory outcomes: after identifying the terms most commonly used by the authors in their scientific articles, text mining and specific algorithms grouped authors with similar research interests (clustering through unsupervised machine learning). Conclusion: As a result of this research, a digital library previously built with traditional data input and display structures now includes a set of intelligent agents that perform processes beyond the trivial filters typically offered—such as searches by author, event, subject, or keywords.
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Copyright (c) 2025 André Luiz de Castro Leal, Sanderson Nascimento Milagres Filho, Lorena Vasconcellos Oliveira Magalhaes, Weslei de Carvalho Vianna, Gizelle Kupac Vianna
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