Knowledge Graphs in Information Systems: A Scopus Bibliometric Analysis of Research Evolution
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Abstract
This study explores the evolution of knowledge graph research within the field of information systems through a comprehensive bibliometric analysis. Data were collected from Scopus and analyzed using quantitative techniques, including performance analysis and science mapping. Visualization was conducted using VOSviewer to examine co-authorship networks, citation structures, and keyword co-occurrence patterns. The results indicate a significant growth in publications, particularly after 2012, reflecting the increasing importance of knowledge graphs in data-driven environments. Co-authorship analysis reveals strong global collaboration, with dominant contributions from countries such as China and the United States. Citation analysis highlights foundational studies in bioinformatics, semantic networks, and graph-based learning as key drivers of the field. Meanwhile, keyword analysis shows a clear thematic shift from ontology and information management toward artificial intelligence, machine learning, natural language processing, and recommender systems. The overlay and density visualizations further confirm the emergence of application-oriented and AI-integrated research trends. Overall, this study provides a structured overview of the intellectual landscape of knowledge graph research and identifies future directions, particularly in the integration of knowledge graphs with advanced artificial intelligence technologies.
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