TCMaster：面向多源中医知识图谱的置信度感知查询与工作负载引导的物理设计
TCMaster: Confidence-Aware Querying and Workload-Guided Physical Design for Multi-Source Traditional Chinese Medicine Knowledge Graphs
- Tsinghua University（清华大学）
- Beijing University of Chinese Medicine（北京中医药大学）
- Guangdong Provincial Laboratory of Traditional Chinese Medicine（广东省中医药实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
TCMaster通过置信度感知查询与工作负载引导的物理设计，提升多源中医知识图谱的查询可靠性与效率，在Neo4j上显著加速查询并提升问答准确率。
AI中文摘要：
多源知识图谱（KGs）需要能够揭示可靠性并利用领域结构的查询机制。本文提出了TCMaster，一个用于中医知识图谱上置信度感知遍历和工作负载引导物理设计的属性图查询基础平台。TCMaster将药典、处方、分子数据库以及大语言模型提取的微语义整合到一个包含约221K个实体和723K条基础边的知识图谱中。它为边标注来源级别的置信度，使用置信度谓词重写Cypher查询，在PRODUCT、MIN或加权平均策略下对多跳路径进行排序，并通过方向选择、草药属性位图和物化捷径边利用本体倾斜。在Neo4j上，方向选择将属性查找提升了1.47倍，捷径将高扇出目标计数加速了4.42倍，置信度过滤移除了39.3%的低质量异质路径，知识图谱检索将TCMbench问答准确率提升了20.0个百分点。
英文摘要：
Multi-source knowledge graphs (KGs) need query mechanisms that expose reliability and exploit domain structure. This paper presents TCMaster, a property-graph query substrate for confidence-aware traversal and workload-guided physical design over Traditional Chinese Medicine KGs. TCMaster integrates pharmacopoeias, prescriptions, molecular databases, and LLM-extracted micro-semantics into a KG with approximately 221K entities and 723K base edges. It annotates edges with provenance-level confidence, rewrites Cypher queries with confidence predicates, ranks multi-hop paths under PRODUCT, MIN, or weighted-average policies, and uses ontology skew through direction selection, herb-attribute bitmaps, and materialized shortcut edges. On Neo4j, direction selection improves attribute lookup by a factor of 1.47, shortcuts accelerate high-fanout target counting by a factor of 4.42, confidence filtering removes 39.3 percent of low-quality heterogeneous paths, and KG retrieval improves TCMbench QA accuracy by 20.0 percentage points.
