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Turkish Journal of Electrical Engineering and Computer Sciences

Author ORCID Identifier

ALI SARABADANI: 0000-0002-3521-1401

KHEIROLLAH RAHSEPAR FARD: 0000-0001-5452-4596

HAMID DALVAND: 0000-0003-2725-5081

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.

DOI

10.55730/1300-0632.4190

Keywords

Knowledge graph, large language model, cognitive neuroscience, cognitive neuroscience knowledge graph, information extraction, link prediction

First Page

542

Last Page

560

Publisher

The Scientific and Technological Research Council of Türkiye (TÜBİTAK)

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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