MultiCNKG: Integrating Cognitive Neuroscience, Gene, and Disease Knowledge Graphs Using Large Language Models

The advent of large language models (LLMs) has revolutionized the integration of knowledge graphs (KGs) in the biomedical and cognitive sciences, effectively overcoming the limitations of traditional machine learning methods in capturing intricate semantic links among genes, diseases, and cognitive processes. This paper introduces MultiCNKG, an innovative framework that merges three distinct knowledge sources: the Cognitive Neuroscience Knowledge Graph (CNKG), containing ۲.۹K nodes and ۴.۳K edges across ۹ node types and ۲۰ edge types; the Gene Ontology (GO), featuring ۴۳K nodes and ۷۵K edges across ۳ node types and ۴ edge types; and the Disease Ontology (DO), comprising ۱۱.۲K nodes and ۸.۸K edges with ۱ node type and ۲ edge types. Utilizing advanced LLMs such as GPT-۴, we perform automated entity alignment, semantic similarity computation, and graph augmentation to construct a unified, cohesive KG that interconnects genetic mechanisms, neurological disorders, and cognitive functions. The resulting MultiCNKG unified graph encompasses ۶.۹K nodes across ۵ distinct types and ۱۱.۳K edges spanning ۷ relational types, establishing a multi-layered analytical pipeline from molecular to behavioral domains. Empirical evaluations demonstrate robust framework performance, achieving an ۸۵.۲۰% precision rate, ۸۷.۳۰% recall, ۹۲.۱۸% coverage, ۸۲.۵۰% graph consistency, a ۴۰.۲۸% novelty detection rate, and an ۸۹.۵۰% expert validation score. Furthermore, link prediction benchmarks utilizing TransE (MR: ۳۹۱, MRR: ۰.۴۱۱) and RotatE (MR: ۲۶۳, MRR: ۰.۳۹۵) yield highly competitive performance against standard benchmarks like FB۱۵k-۲۳۷ and WN۱۸RR. Ultimately, this integrated KG advances clinical and research applications in personalized medicine, cognitive disorder diagnostics, and data-driven hypothesis formulation within cognitive neuroscience.

کلیدواژه ها:

Knowledge Graph ، Large Language Models ، Cognitive neuroscience ، Gene ontology ، Disease Ontology