arXiv cs.AI· Jyotin Goel, Abhinav Arun, Tejas Prakash Agarwal, Bhaskarjit Sarmah, Stefano Pasquali·· 3 小时前AI 评分43
ArcRAG：基于知识图谱的智能体组合式检索
ArcRAG: Agentic Compositional Retrieval over Knowledge Graphs
AI 导读
arXiv 预印本提出 ArcRAG，一种面向知识图谱的智能体组合式检索方法，在推理时根据问题实体归纳查询局部图接口，并以 Cypher 规划、执行和修复多跳遍历。研究在 MuSiQue、HotpotQA、TriviaQA 上分别构建 schema 引导图与开放信息抽取图，验证检索策略无需与图谱构建方式协同设计，在构建方式改变时仍保持有效，但构建方式仍影响绝对准确率。
正文 · 原文
Abstract:Graph-based retrieval-augmented generation (GraphRAG) answers questions by retrieving over a knowledge graph built from a corpus. Yet retrieval strategies are designed around the structure their own construction pipeline produces, and whether they survive a change of construction is rarely tested. We study this cross-construction portability: whether one retrieval procedure, with no construction-specific schema, relation names, or retuning, stays effective when the graph is built differently. We introduce ArcRAG, which induces a query-local graph interface at inference time (the node labels, relation descriptors, and property keys reachable from the entities a question names), plans multi-hop traversals against it, and executes and repairs them as Cypher. We build each of the MuSiQue, HotpotQA, and TriviaQA corpora as a schema-guided graph (typed nodes, a closed relation set, source provenance) and as an open-information-extraction graph (untyped nodes, free-text relations), with the retriever's configuration fixed across them. We compare against a construction-agnostic Direct-Cypher agent, ablate each stage across retry and pass budgets on both graph families, and compare with results reported for prior GraphRAG systems, most of which design retrieval together with their own graph construction. Across the two constructions, we find that retrieval need not be co-designed with the graph: query-time interface induction keeps a construction-agnostic retriever effective when the construction changes, although construction still shapes absolute accuracy.
| Comments: | Preprint |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR) |
| Cite as: | arXiv:2611.06481 [cs.CL] |
| (or arXiv:2611.06481v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2611.06481 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jyotin Goel [view email]
[v1]
Wed, 4 Nov 2026 13:20:51 UTC (2,626 KB)
来源：arXiv cs.AI · arxiv.org