Cnkg: Harnessing Large Language Models for Cognitive Neuroscience Knowledge Graph Construction Publisher



Sarabadani A ; Fard K R ; Dalvand H
Source: Turkish Journal of Electrical Engineering and Computer Sciences Published:2026

Abstract

Textual resources are among the most valuable sources of information in cognitive neuroscience (CN) for understanding and investigating brain activity and cognitive processes. Extracting and constructing knowledge graphs (KGs) from these texts can facilitate medical research by providing deeper insights into neurological diseases and brain function. In recent years, the use of large language models (LLMs) in natural language processing (NLP) has become increasingly widespread, significantly enhancing the extraction of meaningful information from large volumes of text. This study proposes a novel approach for constructing and evaluating a specialized knowledge graph, termed the cognitive neuroscience knowledge graph (CNKG), from scientific publications in the field of CN by leveraging the capabilities of GPT-4. During the construction process, GPT-4 is employed to extract relationships among predefined CN concepts from scientific texts. The resulting graph is then refined to maximize its accuracy and representativeness. Finally, the quality and performance of the CNKG are assessed using GPT-4-based evaluation procedures. The evaluation yielded an accuracy score of 0.936. In addition, link prediction analysis demonstrated that the proposed KG possesses satisfactory quality. Furthermore, complex network metrics obtained using Gephi, particularly the average clustering coefficient (0.419541) and graph diameter (13), provided additional evidence supporting the validity of the constructed graph. The CNKG has the potential to support a variety of downstream applications, including semantic query answering, recommendation systems, and research aimed at the diagnosis and treatment of neurological diseases and disorders. Moreover, it may contribute to improving the quality of research services within the field of cognitive neuroscience. Overall, the proposed approach offers considerable potential to enhance the efficiency and accuracy of cognitive neuroscience literature analysis, thereby opening new avenues for scientific investigation and discovery. © Authors retain the copyright of their works. Upon acceptance, authors grant the journal a non-exclusive right of first publication and permission to distribute the article in all forms and media under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence https://creativecommons.org/licenses/by/4.0/.