Clarifying and consolidating graph-based knowledge representations in LIS: a terminological framework

Authors

DOI:

https://doi.org/10.5007/1518-2924.2026.e108640

Keywords:

ontology, knowledge graph, causality graph, semantic network, semantic graph

Abstract

Objective: the present study aims to consolidate key terms related to graph-based knowledge representation, such as knowledge graph, semantic network, semantic graph and ontology, by examining how they are defined and used in scientific literature.

Methods: a systematic literature review was conducted, retrieving 474 studies published between 2019 and 2023 from major scientific repositories, of which 288 met the inclusion criteria and were analyzed.

Results: the analysis reveals substantial overlap in the conceptualization of graph-based representation terms while also exposing important nuances. Ontologies remain foundational within Library and Information Science, being often associated with domain modeling and semantic integration. In contrast, knowledge graphs have experienced rapid growth in recent years, particularly in applied contexts involving artificial intelligence and data fusion. Other terms, such as semantic networks and semantic graphs, are used less consistently and often lack formal definitions, indicating fragmentation in terminology usage.

Conclusions: this study advances the theoretical understanding of graph-based knowledge representations by clarifying terminological boundaries and intersections. It offers a consolidated framework that supports conceptual alignment across disciplines, promoting more coherent usage in future research and applications.

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Author Biographies

Rafael Rocha, University of Minas Gerais

PhD Candidate in the Graduate Program in Knowledge Management and Organization (PPGGOC) at the Federal University of Minas Gerais (UFMG). He holds a Master’s degree in Knowledge Management and Organization from UFMG (2021), a specialization in Web Systems Development from PUC Minas (2010), and a Bachelor’s degree in Data Processing Technology from Fabrai (2007). His academic and professional trajectory is dedicated to studies in Information Science, with an emphasis on knowledge management, organization, and visualization. He is a member of the MHTX Research Group – Conceptual Modeling for the Organization and Representation of Hypertextual Information.

Gercina Ângela de Lima, University of Minas Gerais

Full Professor in the Department of Information Organization and Processing at ECI/UFMG. PhD in Information Science from the Graduate Program in Information Science (PPGCI-ECI/UFMG). Master in Science in Library Service – Clark Atlanta University. Librarian from the School of Library Science at UFMG. Research Productivity Fellow of CNPq (PQ) Level 1D. Fellow of the Minas Gerais Researcher Program (PPM XII-2018) of FAPEMIG. Researcher in the Graduate Program in Knowledge Management & Organization (PPGGOC/ECI/UFMG). Coordinator of the Research Group MHTX – Conceptual Modeling for the Organization and Representation of Hypertextual Information, registered with CNPq since 2004.

Webert Júnio Araújo, University of Minas Gerais

PhD in Knowledge Management and Organization from the Federal University of Minas Gerais.

Patrícia Lopes, University of Minas Gerais

PhD in Knowledge Management and Organization from the Federal University of Minas Gerais. Master’s degree in Knowledge Management and Organization from the Federal University of Minas Gerais (2018). Specialist in Public Management from the Federal University of Rio Grande do Sul (2014), Specialist in Instructional Design for Virtual Distance Education from the Federal University of Itajubá (2012), Specialist in Digital Media Production from PUC Minas (2010), Specialist in Distance Education from Senac Minas (2009), and Bachelor’s degree in Pedagogy from the University Center of Belo Horizonte (2007). She has worked as a Pedagogical Supervisor and Instructional Designer at Senac Minas. Currently, she is an analyst at the Superintendence of Education, Talent, and Culture of the Federal Data Processing Service – SERPRO. Member of the Research Group MHTX – Conceptual Modeling for the Organization and Representation of Hypertextual Information.

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2026-02-25

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ROCHA, Rafael; LIMA, Gercina Ângela de; ARAÚJO, Webert Júnio; LOPES, Patrícia. Clarifying and consolidating graph-based knowledge representations in LIS: a terminological framework. Encontros Bibli: electronic journal of library science, archival science and information science, Florianópolis/SC, Brasil, v. 31, p. 1–26, 2026. DOI: 10.5007/1518-2924.2026.e108640. Disponível em: https://periodicos.ufsc.br/index.php/eb/article/view/108640. Acesso em: 29 apr. 2026.

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Vol. 31 (2026): Ethics and informational integrity in the climate emergency: the socio-environmental responsibility of Information Science.

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