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A Unified LLM KG Framework For Low Annotation Urban Rail Transit Signal System Operation Knowledge Acquisition and Dynamic Update by chafaaamine9 is a document available to read on EtoBox.
The paper presents a unified large language model-knowledge graph framework (ULLM-KG) designed for the intelligent operation and maintenance of urban rail transit signal systems (URTSS) under low-annotation conditions. It introduces several innovative methods for knowledge graph construction, entity extraction, and dynamic updating to enhance knowledge management and fault diagnosis. Experimental results demonstrate that ULLM-KG significantly outperforms existing methods in knowledge extraction and reasonin
- Author
- chafaaamine9
- Language
- EN