ExKG-LLM: Leveraging Large Language Models for Automated Expansion of Cognitive Neuroscience Knowledge Graphs

2025-03-09

AI Summary

To address low accuracy, narrow coverage, and infrequent updates in Cognitive Neuroscience Knowledge Graph (CNKG) expansion, this paper proposes the first LLM-driven incremental construction framework tailored for CNKGs. The method integrates large language model–based joint entity-relation extraction, semantic consistency–constrained relation inference, knowledge graph embedding optimization, and dynamic graph structure integration—enabling efficient, scalable, and semantically faithful graph updates. Experimental results demonstrate significant improvements: precision reaches 0.80 (+6.67%), recall 0.81 (+15.71%), and F1-score increases by 11.81%. Node and edge counts grow by over 21%, while user interaction rates in clinical decision support scenarios rise by 20%. This work establishes a reusable methodology and technical paradigm for automated, iterative evolution of domain-specific knowledge graphs.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Knowledge Representation and Reasoning: Knowledge Acquisition

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: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The paper introduces ExKG-LLM, a framework designed to automate the expansion of cognitive neuroscience knowledge graphs (CNKG) using large language models (LLMs). It addresses limitations in existing tools by enhancing accuracy, completeness, and usefulness in CNKG. The framework leverages a large dataset of scientific papers and clinical reports, applying state-of-the-art LLMs to extract, optimize, and integrate new entities and relationships. Evaluation metrics include precision, recall, and graph density. Results show significant improvements: precision (0.80, +6.67%), recall (0.81, +15.71%), F1 score (0.805, +11.81%), and increased edge nodes (21.13% and 31.92%). Graph density slightly decreased, reflecting a broader but more fragmented structure. Engagement rates rose by 20%, while CNKG diameter increased to 15, indicating a more distributed structure. Time complexity improved to O(n log n), but space complexity rose to O(n2), indicating higher memory usage. ExKG-LLM demonstrates potential for enhancing knowledge generation, semantic search, and clinical decision-making in cognitive neuroscience, adaptable to broader scientific fields.

Problem

• Automates expansion of cognitive neuroscience knowledge graphs
• Improves accuracy, completeness, and usefulness of CNKG
• Enhances knowledge generation and clinical decision-making

Innovation

• Automates cognitive neuroscience knowledge graph expansion
• Uses large language models for entity extraction
• Improves precision, recall, and F1 scores significantly