用大模型自动扩展认知神经科学知识图谱，提升准确性和完整性。
ExKG-LLM: Leveraging Large Language Models for Automated Expansion of Cognitive Neuroscience Knowledge Graphs
- 利用大模型从文献和病历中自动提取并整合新实体与关系。
- 精度达0.80（+6.67%），召回率0.81（+15.71%），F1提升11.81%。
- 适合科研人员构建知识图谱，推动临床决策与语义搜索发展。
本文提出ExKG-LLM框架，利用大语言模型（LLMs）自动化扩展认知神经科学知识图谱（CNKG）。该框架基于大规模科学论文与临床报告数据集，采用先进LLM提取、优化并集成新的实体与关系。评估指标包括精确率、召回率和图密度。结果表明：精确率提升至0.80（+6.67%），召回率提升至0.81（+15.71%），F1分数达0.805（+11.81%），边节点增加21.13%与31.92%。图密度略有下降，反映结构更广但更碎片化；用户参与度上升20%，知识图谱直径增至15，显示分布更分散。时间复杂度优化至O(n log n)，空间复杂度升至O(n²)，内存占用更高。实验验证了其在知识生成、语义搜索及临床决策中的潜力，并具备向更广科学领域拓展的适应性。
原文摘要 · Abstract (English)
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.
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