用大模型构建中风知识图谱，提升医学文献信息提取精度。
SKG-LLM: Developing a Mathematical Model for Stroke Knowledge Graph Construction Using Large Language Models
- 结合数学建模与GPT-4，自动从文献中提取中风相关关系。
- 构建含2692个节点、5012条边的知识图谱，精确率达0.906，召回率达0.923。
- 适合医学信息挖掘、智能诊疗系统研发者参考。
本研究提出SKG-LLM，利用数学模型与大语言模型（LLMs）从卒中相关文献中构建知识图谱（KG），以提升卒中研究中知识的准确性和深度。方法中采用GPT-4进行数据预处理及嵌入提取。通过精确率（Precision）和召回率（Recall）评估模型性能。相较于Wikidata和WN18RR，SKG-LLM表现更优，尤其在精确率与召回率方面。引入专家评审后，精确率提升至0.923，召回率提升至0.918。最终构建的知识图谱包含2692个节点、5012条边，涵盖13种节点类型和24种边类型。
原文摘要 · Abstract (English)
The purpose of this study is to introduce SKG-LLM. A knowledge graph (KG) is constructed from stroke-related articles using mathematical and large language models (LLMs). SKG-LLM extracts and organizes complex relationships from the biomedical literature, using it to increase the accuracy and depth of KG in stroke research. In the proposed method, GPT-4 was used for data pre-processing, and the extraction of embeddings was also done by GPT-4 in the whole KG construction process. The performance of the proposed model was tested with two evaluation criteria: Precision and Recall. For further validation of the proposed model, GPT-4 was used. Compared with Wikidata and WN18RR, the proposed KG-LLM approach performs better, especially in precision and recall. By including GPT-4 in the preprocessing process, the SKG-LLM model achieved a precision score of 0.906 and a recall score of 0.923. Expert reviews further improved the results and increased precision to 0.923 and recall to 0.918. The knowledge graph constructed by SKG-LLM contains 2692 nodes and 5012 edges, which are 13 distinct types of nodes and 24 types of edges.
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