Title: Fuzzy retrieval of power dispatching knowledge base through large language model integrated with knowledge graph

Authors: Shuhong Wu

Addresses: Guangdong Power Grid Corporation, Foshan Power Supply Bureau, Foshan, 528000, China

Abstract: In order to solve the problems of low normalised loss accumulation gain and low retrieval consistency in traditional methods, a fuzzy retrieval method of power dispatching knowledge base through large language model integrated with knowledge graph is proposed. The entity and attribute information are carefully classified by triple algorithm and the text knowledge graph of power dispatching is constructed. StanfordNLP is used to analyse the part-of-speech of text data related to power dispatching, and the invalid triples are removed. The large language model and knowledge graph are fused to construct the power dispatching knowledge base, and the natural language is mapped by fuzzy reasoning mechanism, and the data information with the highest fitting degree is taken as the output result of retrieval. Experiments show that the normalised cumulative loss gain of this method is close to 1, the retrieval consistency is always higher than 90%, the retrieval results are reliable.

Keywords: large language model; knowledge graph; power dispatch; knowledge base; triple.

DOI: 10.1504/IJITM.2025.151550

International Journal of Information Technology and Management, 2025 Vol.24 No.3/4, pp.237 - 258

Received: 19 Sep 2024
Accepted: 07 Feb 2025
Published online: 05 Feb 2026 *

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