Topical Evolution and Thematic Progression of Research Frontiers: The Field of Knowledge Graphs

The paper aimed to analyze the thematic growth and topical evolution of the intellectual structure in Knowledge Graphs (kGs) during 2013–2023. This applied research used an analytical and descriptive method, co-word techniques, scientometric indicators, and social network analysis. A web-based interface of Bibliometrix, Python, Microsoft Excel, VOSviewer, UCINet, and SPSS was used for descriptive bibliometric study, data analysis, and network structure visualization. China was the most prolific country. Nine major topic clusters were identified based on the co-occurrence network. The most mature and mainstream thematic cluster was the ‘application of knowledge-based systems’. Six clusters were identified in the network structure: ‘knowledge graph’, ‘knowledge engineering’, and ‘knowledge discovery’ as niche themes, and ‘ontology’, ‘semantic web’, and ‘linked data’ as basic themes. Moreover, six main cluster evolutions during 2021–2023 were identified: ‘ontology’, ‘natural language processing’, ‘machine learning’, ‘protein’, and ‘knowledge engineering’, and ‘knowledge graph’.