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

Shuhong Wu

International Journal of Information Technology and Management2025https://doi.org/10.1504/ijitm.2025.151550article
AJG 1
Weight
0.50

What the paper says

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.

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https://doi.org/https://doi.org/10.1504/ijitm.2025.151550

Or copy a formatted citation

@article{shuhong2025,
  title        = {{Fuzzy retrieval of power dispatching knowledge base through large language model integrated with knowledge graph}},
  author       = {Shuhong Wu},
  journal      = {International Journal of Information Technology and Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijitm.2025.151550},
}

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

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.