接入 API · 个人 AI 解读连接自己的模型解读资讯,浏览新闻无需配置。
返回资讯列表
前沿研究社区 / 第三方国际

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

arXiv 人工智能论文 · 发布
今日摘要使用自己的 API,仅供个人查看

来源摘要

arXiv:2609.25712v1 Announce Type: new Abstract: 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.

阅读原始来源
来源
arXiv 人工智能论文 · 社区 / 第三方
来源发布
2026/09/23 12:00
首次采集
2026/09/23 17:59

本文为公开信息索引与摘要,详情及后续变化请以原始来源为准。

把 AI 雷达放到桌面

在支持安装的浏览器中,可以将本站作为应用打开。

安装入口取决于浏览器;应用和网站使用同一份最新内容。

查看完整安装指南