A Bibliometric Analysis on the Integration of Large Language Models and Knowledge Graphs: Exploring Trends, Techniques, and Emerging Directions

Wafa Ghemmaz; Maroua Bouzid; Souad Bouaicha; Naila Marir

doi:10.22456/2175-2745.151034

Authors

Wafa Ghemmaz University of Constantine 2 - Abdelhamid Mehri (UC2) https://orcid.org/0000-0002-8320-6834
Maroua Bouzid University of Caen Lower Normandy (UNICAEN)
Souad Bouaicha University of Constantine 2 - Abdelhamid Mehri (UC2) https://orcid.org/0009-0003-4594-0231
Naila Marir Effat University (EU) https://orcid.org/0000-0001-6633-9099

DOI:

https://doi.org/10.22456/2175-2745.151034

Keywords:

knowledge graphs, large language models, research trends, emerging topics, VOSviewer

Abstract

In recent years, the integration of large language models and knowledge graphs has increasingly attracted the attention of both researchers and practitioners. While existing reviews provide valuable and helpful insights for better understanding the research field, they lack a quantitative perspective. To address this limitation, a bibliometric analysis was performed in this study to fill the gap. In particular, sourcing data from the Web of Science and Scopus databases, our analysis focuses on: (1) publication and citation trends, (2) the most productive countries and authors, (3) the most influential sources and scientific papers, (4) the main research tasks particularly in KG-enhanced LLMs and LLM-augmented KGs, (5) the most frequently employed techniques and their evolution over time. In addition to providing a comprehensive overview of the most active and recent areas of research, this study identifies several emerging themes, namely: “Knowledge-aware Prompt Engineering”, “Retrieval-Augmented Generation for LLM–KG Integration”, “Knowledge Graph-based Hallucination Detection” and the convergence of “Multimodal Knowledge Graphs and Multimodal Large Language Models”. These insights contribute to a clearer understanding of the field's development and highlight emerging research trends grounded in bibliometric evidence.

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References

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