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

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.

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

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.