通过图模型融合工具与领域知识，提升智能规划生成效果。
Bridging Tool Dependencies and Domain Knowledge: A Graph-Based Framework for In-Context Planning
- 构建工具与文档双知识图谱，关联工具接口与操作流程。
- 融合结构依赖与流程知识，生成更合理的计划路径。
- 适合需要复杂工具调用的自动化决策系统开发者使用。
我们提出一种框架，通过挖掘工具与文档间的依赖关系，提升示例性任务规划的生成质量。方法首先基于工具定义（含描述、参数、输出）构建工具知识图谱，采用受DeepResearch启发的分析方式；同时从内部文档和标准操作流程（SOPs）中提取互补的知识图谱，并将其与工具图谱融合。为生成示例计划，采用深度稀疏集成策略，对齐工具的结构依赖与操作流程知识。实验表明，该统一框架能有效建模工具间交互，显著提升计划生成能力，验证了将工具图谱与领域知识图谱结合在工具增强推理与规划中的优势。
原文摘要 · Abstract (English)
We present a framework for uncovering and exploiting dependencies among tools and documents to enhance exemplar artifact generation. Our method begins by constructing a tool knowledge graph from tool schemas,including descriptions, arguments, and output payloads, using a DeepResearch-inspired analysis. In parallel, we derive a complementary knowledge graph from internal documents and SOPs, which is then fused with the tool graph. To generate exemplar plans, we adopt a deep-sparse integration strategy that aligns structural tool dependencies with procedural knowledge. Experiments demonstrate that this unified framework effectively models tool interactions and improves plan generation, underscoring the benefits of linking tool graphs with domain knowledge graphs for tool-augmented reasoning and planning.
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