Bibliometric Analysis and Co-word Mapping: The Field of Knowledge Graphs

Abstract

Purpose: The rapid expansion of research in the field of Knowledge Graphs (KGs) over the past decade has positioned The field as a dynamic area at the intersection of computer science, artificial intelligence. Following Google’s introduction of the KG in 2012, scholars and industry stakeholders have increasingly explored the conceptualization, development, and application of KGs across a wide range of disciplines. KGs, as structured representations of entities, relationships, and semantic information, enable machines to process heterogeneous data in a contextually meaningful and interpretable manner. Beyond traditional knowledge representation, new directions such as ontology modeling, semantic web integration, and graph neural networks (GNNs) are driving innovation. Despite these advances, large-scale, systematic, and global evaluations of the intellectual structure, collaboration patterns, and thematic clusters in the field of KGs remain limited. This study addresses this gap by examining the evolution of research in the field of KGs between 2013 and 2025 using bibliometric and scientometric approaches. The overarching purpose is to uncover research trends, identify leading contributors and institutions, explore co-authorship and co-word networks, and highlight conceptual clusters shaping the KG landscape. Methodology: This applied descriptive bibliometric study was conducted using the co-word analysis method. Data were collected from the Scopus citation database, covering outputs published between January 2013 to July 2025 that included the keyword "knowledge graph" in titles, abstracts, or author keywords. Keyword normalization reduced 19,141 initial keywords to 19,058 unique terms. After removing duplicates, excluding irrelevant records, and normalizing author, institution, and country names, a curated dataset of 9,000 articles was obtained. To enhance the accuracy of the analysis, keywords and knowledge graph-related concepts were carefully categorized and standardized. Data analysis and visualization were performed using VOSviewer, mapping collaboration networks and co-occurrence clusters. The study examines temporal publication and citation trends, prolific countries, funding bodies, influential authors, highly cited works, and thematic clusters, with particular focus on knowledge graph embedding, recommender systems, semantic web applications, and natural language processing. Findings: Results indicate that research in the field of knowledge graphs (KGs) has grown substantially, reaching a publication peak in 2024 with 2,541 articles and 33,701 citations. Asia contributes the majority of publications (83.7%), with China accounting for 6,889 articles (76.5% of the total). The United States ranks second with 987 publications, followed by the United Kingdom, Germany, and Australia. This trend reflects strong investment by Chinese funding bodies—particularly the National Natural Science Foundation of China (NSFC) and the National Key Research and Development Program. Leading publication outlets include IEEE Access (345 articles), Knowledge-Based Systems (293 articles), and Applied Sciences Switzerland (248 articles), with many top journals in the Q1 quartile of Scopus. At the author level, Markus Kraft (University of Cambridge) leads with 48 publications, while Chinese researchers dominate the top ten. Co-authorship analysis revealed seven author clusters (42 nodes, 62 links) and broad participation from 136 countries, with China, the US, and Germany serving as central hubs of international collaboration. Co-word analysis identified six major clusters, including graph neural networks, deep learning, ontology, KG embeddings, question answering, and large language models (LLMs). Highly cited works include comprehensive surveys on KGs published between 2017 and 2022 in leading computing journals, which have shaped the understanding of KG construction, refinement, and applications. . Emerging contributions, such as roadmaps on unifying LLMs and KGs, along with applications in digital twin systems and scientific entity recognition, illustrate the field’s ongoing shift toward integrating advanced AI paradigms. Conclusion: The bibliometric analysis reveals that research in the field of knowledge graphs (KGs) has rapidly emerged as a dynamic interdisciplinary area, characterized by strong publication growth, a high geographic concentration in Asia, and expanding international collaboration networks. Chinese institutions and scholars play a leading role, supported by significant policy and funding initiatives, while Europe and North America remain important contributors. Thematic clustering highlights both the consolidation of established domains—such as ontology, the semantic web, and knowledge representation—and expansion into cutting-edge areas, including graph neural networks, embeddings, digital twins, recommender systems, and large language model (LLM) integration. These findings provide a comprehensive overview of the intellectual structure and evolving dynamics of research in KGs, offering valuable insights for future studies. Scholars can use this mapping to identify promising areas of inquiry, foster international collaborations, and navigate the funding and publication landscape. Ultimately, KGs play a pivotal role in advancing intelligent systems, semantic technologies, and digital transformation across sectors such as healthcare, education, natural language processing, and recommender systems. The integration of bibliometric analysis with scientometric evaluation further underscores the maturity of this field and its potential to shape next-generation knowledge-driven applications.