LLM-Driven Unsupervised Discovery of Semantic Decision Dimensions for Dynamic Scheduling and Intelligent Systems: A Traditional Chinese Medicine Case Study

Dynamic scheduling systems often rely on structured domain knowledge for real-time optimization and decision making. However, much of this knowledge originates from unstructured text, such as operational manuals, maintenance logs, and expert records. Extracting machine readable decision categories from such raw corpora without manual intervention remains a significant challenge. To address this issue, we propose a framework using large language models (LLMs) that automatically discovers latent semantic dimensions from unstructured text without any predefined schema. The LLM performs unsupervised semantic clustering via hierarchical prompts, autonomously inducing concept categories. We validate our method on Traditional Chinese Medicine (TCM), a domain characterized by highly nested, multi-dimensional knowledge structures. This makes it a challenging and representative benchmark for testing the generalizability of knowledge discovery methods. From 12,163 TCM herb records (64,107 independent short sentences), the DeepSeek-V3.2 model with batch-wise clustering generates 458 raw labels. These labels are further merged into 22 broad semantic categories. The resulting groups can be interpreted as potential decision dimensions or state abstractions for knowledge driven optimization systems. For evaluation, we adopt a dual approach: (1) semantic cosine similarity computed by Qwen3-Embedding-8B shows that 19 concepts achieve an average internal coherence above 60%; (2) domain experts rate the reasonableness of sampled concepts on a 1 to 5 scale, yielding an average score of 4.64. Experimental results demonstrate that our method autonomously induces high quality, interpretable concept taxonomies from large-scale unstructured text. We further validate the transferability of the method on an aircraft assembly manual, where it induces 12 semantically coherent concepts (average example coherence 59.6%) that align well with a domain ontology. This work offers a transferable paradigm for integrating unstructured domain knowledge into intelligent scheduling and optimization frameworks, with potential implications for dynamic scheduling systems.