SKG-LLM: Developing a Mathematical Model for Stroke Knowledge Graph Construction Using Large Language Models

Ali Sarabadani; Kheirolah Rahsepar Fard; Hamid Dalvand

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arXiv · 2503.06475

SKG-LLM: Developing a Mathematical Model for Stroke Knowledge Graph Construction Using Large Language Models

Ali Sarabadani·Kheirolah Rahsepar Fard·Hamid Dalvand

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Abstract

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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Ali Sarabadani, Kheirolah Rahsepar Fard, Hamid Dalvand. 2025-03-09. SKG-LLM: Developing a Mathematical Model for Stroke Knowledge Graph Construction Using Large Language Models. https://arxiv.org/abs/2503.06475

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