让非专家也能轻松查询企业知识图谱，准确率提升7倍。
SEGRA: A Structured Experience Guided Reasoning Agent for Property Graph Question Answering
- 基于经验的智能代理，自动解析问题并生成正确Gremlin查询。
- 在企业数据集上准确率比基础方法高7倍，减少20%调用次数。
- 适合需要高效查询知识图谱的企业运维人员使用。
企业IT支持知识图谱包含了案例、用户、设备、症状、分类类别、根本原因和历史解决方案之间的丰富关系。然而，使用Gremlin查询这些图谱需要掌握图模式、遍历语义、边的方向性及属性图特有约束，对非专家操作员而言难度较高。我们提出SEGRA，一种面向企业文本到Gremlin问答的经验引导型智能体。SEGRA融合意图路由、基于模式与分类体系的查询生成、多示例分解、执行感知验证，以及基于课程学习的技能库，可复用已验证的查询模式。在企业IT支持基准测试中，SEGRA的平均评分比仅使用链式思维提示的基线高出7.0倍。其技能库进一步将LLM调用减少20%，成本降低18%，同时保持答案质量。结果表明，基于模式的智能体设计与可复用的执行经验能显著提升企业图谱问答的准确性与效率。
原文摘要 · Abstract (English)
Enterprise IT support knowledge graphs capture rich relationships among cases, users, devices, symptoms, taxonomic categories, root causes, and historical resolutions. Yet querying them in Gremlin requires knowledge of graph schemas, traversal semantics, edge directionality, and property-graph-specific constraints, making them difficult for non-expert operators to use. We introduce SEGRA, an experience-guided agent for enterprise text-to-Gremlin question answering. SEGRA integrates intent routing, schema- and taxonomy-grounded query generation, multi-shot decomposition, execution-aware verification, and a curriculum-bootstrapped skill library that reuses verified query patterns. On an enterprise IT support benchmark, SEGRA achieves a $7.0\times$ higher mean judge score than backbone-only chain-of-thought prompting. Its skill library further reduces LLM calls by $20\%$ and dollar cost by $18\%$ relative to SEGRA without skills, while preserving answer quality. These results show that schema-grounded agent design and reusable execution experience improve both accuracy and efficiency for enterprise graph QA.
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