Construction of a Tibetan Medicine Typical Disease Knowledge Graph

Tibetan medicine, as one of the oldest medical systems in the world, is an integral part of traditional Chinese medicine. However, its knowledge is primarily preserved in traditional formats, limiting its digitization, structuring, and intelligent analysis. In this paper, we construct a structured dataset for Tibetan medicine, covering 1,962 diseases, 125 causes, 3,919 symptoms, and 995 treatment methods. Existing Named Entity Recognition (NER) methods typically overlook the uniqueness of the Tibetan medicine domain and fail to fully account for the differences between Tibetan medicine and general domains. To better adapt the model to this specific domain, we introduced a domain-aware prompt fine-tuning strategy. Experimental results show that our model achieved an F1 score of 93.33% in the Tibetan medicine domain, with an improvement of at least 5.2% compared to the baseline model. Ultimately, we constructed a Tibetan medicine disease knowledge graph consisting of 7,139 entity nodes and 9,300 entity relationships, providing a structured representation of Tibetan medical knowledge. We also performed data analysis using similarity calculation and topological link prediction algorithms, advancing its intelligent analysis process.