Clarifying and consolidating graph-based knowledge representations in LIS: a terminological framework
DOI:
https://doi.org/10.5007/1518-2924.2026.e108640Keywords:
ontology, knowledge graph, causality graph, semantic network, semantic graphAbstract
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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