ExKG-LLM: Leveraging Large Language Models for Automated Expansion of Cognitive Neuroscience Knowledge Graphs
This work addresses the need for more accurate and complete knowledge graphs in cognitive neuroscience, with potential applications in semantic search and clinical decision-making, though it appears incremental as it builds on existing LLM methods for a specific domain.
The paper tackles the problem of automating the expansion of cognitive neuroscience knowledge graphs by introducing ExKG-LLM, a framework that uses large language models to extract and integrate new entities and relationships, resulting in improvements such as precision of 0.80 (+6.67%), recall of 0.81 (+15.71%), F1 score of 0.805 (+11.81%), and increased edge nodes by 21.13% and 31.92%.
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.