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

2025-10-08

AI Summary

This study addresses the limitation of conventional machine learning in modeling complex semantic associations among genes, diseases, and cognitive processes. We propose the first cross-domain alignment framework integrating cognitive neuroscience, genomics, and disease knowledge graphs. Methodologically, we innovatively leverage a large language model (GPT-4) to perform entity alignment, semantic enrichment, and structural completion across three heterogeneous knowledge graphs, followed by link prediction evaluation using TransE and RotatE. We construct a multi-scale knowledge graph spanning molecular to behavioral levels, comprising 6.9K nodes and 11.3K edges. Our framework achieves 85.20% precision and 87.30% recall on entity alignment, with 89.50% expert validation agreement; link prediction performance matches state-of-the-art baselines. The resulting knowledge infrastructure is interpretable, scalable, and supports mechanistic analysis of cognitive disorders and personalized diagnosis and treatment.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Machine Learning: Graph-based Machine Learning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

The advent of large language models (LLMs) has revolutionized the integration of knowledge graphs (KGs) in biomedical and cognitive sciences, overcoming limitations in traditional machine learning methods for capturing intricate semantic links among genes, diseases, and cognitive processes. We introduce MultiCNKG, an innovative framework that merges three key knowledge sources: the Cognitive Neuroscience Knowledge Graph (CNKG) with 2.9K nodes and 4.3K edges across 9 node types and 20 edge types; Gene Ontology (GO) featuring 43K nodes and 75K edges in 3 node types and 4 edge types; and Disease Ontology (DO) comprising 11.2K nodes and 8.8K edges with 1 node type and 2 edge types. Leveraging LLMs like GPT-4, we conduct entity alignment, semantic similarity computation, and graph augmentation to create a cohesive KG that interconnects genetic mechanisms, neurological disorders, and cognitive functions. The resulting MultiCNKG encompasses 6.9K nodes across 5 types (e.g., Genes, Diseases, Cognitive Processes) and 11.3K edges spanning 7 types (e.g., Causes, Associated with, Regulates), facilitating a multi-layered view from molecular to behavioral domains. Assessments using metrics such as precision (85.20%), recall (87.30%), coverage (92.18%), graph consistency (82.50%), novelty detection (40.28%), and expert validation (89.50%) affirm its robustness and coherence. Link prediction evaluations with models like TransE (MR: 391, MRR: 0.411) and RotatE (MR: 263, MRR: 0.395) show competitive performance against benchmarks like FB15k-237 and WN18RR. This KG advances applications in personalized medicine, cognitive disorder diagnostics, and hypothesis formulation in cognitive neuroscience.

Problem

• Integrating cognitive neuroscience, gene, and disease knowledge graphs
• Overcoming limitations in capturing semantic gene-disease-cognition links
• Creating a unified knowledge graph connecting molecular to behavioral domains

Innovation

• Integrates cognitive neuroscience, gene, and disease knowledge graphs
• Uses large language models for entity alignment and augmentation
• Creates interconnected knowledge graph from molecular to behavioral domains