SEGRA: A Structured Experience Guided Reasoning Agent for Property Graph Question Answering
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 ent...
| Authors | Saiyue Lyu, Mariam Dundua, Vishaal Kapoor et al. |
|---|---|
| Primary category | cs.AI |
| Published | 21 July 2026 |
| Last updated at source | 28 August 2026 |
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