AutoSchema：面向异构知识图谱的智能体文本转SPARQL的实时模式接地
AutoSchema: Live Schema Grounding for Agentic Text-to-Sparql over Heterogeneous Knowledge Graphs
- The University of Tokyo（东京大学）
- National Institute for Materials Science（国立材料科学研究所）
- RIKEN Center for Advanced Intelligence Project（理化学研究所高级智能项目中心）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
提出无需训练的AutoSchema框架，用于异构知识图谱的智能体文本转SPARQL的实时模式接地，在多项生物医学KGQA等任务中优于TogoMCP，可支持未记录RDF图谱。
中文摘要 AI 辅助
生命科学知识图谱通过SPARQL提供大量结构化数据集合，但每个资源都有各自的模式、标识符和链接。TogoMCP通过提供经过整理的元数据互操作性交换文件，帮助语言模型智能体查询这些资源；但创建和维护这些文件仍需要语言模型辅助起草、验证及人工审核。本文研究“实时模式接地”，即智能体直接从当前端点获取问题所需的模式证据。我们提出AutoSchema（通用实时模式接地框架，无需训练），其可检查实时模式、将问题中的实体名称映射到图谱标识符、探索关系路径，并在迭代构建查询时寻找资源间的可能连接。我们以TogoMCP为主要对比框架，在资源聚焦生物医学KGQA、多资源生物医学KGQA、BioASQ任务B的纵向生物医学语义QA，以及化学知识图谱向未记录RDF图谱的迁移任务上评估AutoSchema。结果显示，AutoSchema在生物医学KGQA任务上的平均事实准确率优于TogoMCP，在纵向BioASQ评估中也取得持续提升；其还减少了迭代预算耗尽的情况，且核心评估中平均工具调用次数更少。迁移研究初步证明，实时模式接地无需先创建整理后的模式文件，即可支持不规则及此前未见过的图谱。
英文摘要
Life science knowledge graphs make large collections of structured data available through SPARQL, but each resource uses its own schema, identifiers, and links. TogoMCP helps language model agents query these resources by providing curated Metadata Interoperability Exchange files. Creating and maintaining these files still requires language model assisted drafting, validation, and manual review. We study \emph{live schema grounding}, where an agent obtains the schema evidence needed for a question directly from the current endpoints. We present \textsc{autoschema}, a general framework for live schema grounding that requires no training. It inspects live schemas, maps entity names in a question to graph identifiers, explores relation paths, and finds possible connections between resources during iterative query construction. We use TogoMCP as our main comparison framework. We evaluate \textsc{autoschema} on Resource Focused Biomedical KGQA, Multi Resource Biomedical KGQA, Longitudinal Biomedical Semantic QA over BioASQ Task B, and Chemistry Knowledge Graph Transfer to a previously undocumented RDF graph. \textsc{autoschema} improves mean factoid accuracy over TogoMCP in the biomedical KGQA tasks and gives consistent gains in the longitudinal BioASQ evaluation. It also reduces iteration budget exhaustion and uses fewer tool calls on average in the core evaluation. The transfer study gives preliminary evidence that live schema grounding can support irregular and previously unseen graphs without first creating a curated schema file.
