TCMaster: Confidence-Aware Querying and Workload-Guided Physical Design for Multi-Source Traditional Chinese Medicine Knowledge Graphs

arXiv:2609.25712 · cs.AI · Submitted 2026-09-22 · Read on arXiv

cs.AI

Submitted: 2026-09-22

Updated: 2026-09-22

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Provenance — 34× in this paper · explained in 4 episodes
• Ontology — 32× in this paper · explained in 2 episodes
• RAG — 19× in this paper · explained in 3 episodes
• Knowledge Graph — 18× in this paper · explained in 7 episodes
• Aggregation — 17× in this paper · explained in 1 episode
• Benchmark — 13× in this paper · explained in 2 episodes
• Traditional Chinese Medicine — 12× in this paper
• Accuracy — 9× in this paper · explained in 1 episode
• Pruning — 8× in this paper · explained in 4 episodes
• Core — 7× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Knowledge graphs — 5× in this paper · explained in 2 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Hallucination — 4× in this paper · explained in 3 episodes
• Aggregation Function — 3× in this paper · explained in 1 episode
• Verification — 3× in this paper · explained in 2 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• OpenTCM: A GraphRAG-Empowered LLM-based System for Traditional Chinese Medicine Knowledge Retrieval and Diagnosis
• TCMBench: A Comprehensive Benchmark for Evaluating Large Language Models in Traditional Chinese Medicine
• From Local to Global: A Graph RAG Approach to Query-Focused Summarization

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