PolyUQuest: Verifiable Structure-Aware Web RAG over Heterogeneous Graphs
On the map: Retrieval-augmented generation → Knowledge-graph and structured-knowledge RAG
Abstract (arXiv)
Existing retrieval-augmented generation (RAG) systems treat web pages as flat text, losing the structural and semantic signals encoded in HTML. We present PolyUQuest, a verifiable, structure-aware web RAG framework built on a heterogeneous graph that unifies hyperlink topology between pages, DOM hierarchy within pages, and entity-relation knowledge across pages. A two-tier router dispatches each query to one of three retrieval modes matched to its structural need, including direct block retrieval, cross-page graph traversal, and multi-hop entity reasoning. Each answer carries traceable provenance: every cited block records its source page, heading path, and entity links, so users can inspect the structural evidence behind a claim. We evaluate on the official websites of the Hong Kong Polytechnic University (PolyU), comprising 4,240 pages, 31,086 DOM blocks, 29,119 entities, and 37,680 relations, together with a multi-type evaluation benchmark. PolyUQuest improves correctness, coverage, and faithfulness over the evaluated baselines while maintaining query-time token consumption comparable to ChunkRAG and substantially below the graph-based RAG baselines. The demonstration provides an interactive interface for inspecting cited answers, comparing retrieval traces across routing modes, and exploring evidence graph paths. PolyUQuest is being prepared for deployment as a student-facing QA service at PolyU.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation. Builds on 11 of its 29 references that are in the map. First posted on arXiv 9 Jul 2026.
Builds on
11 of 29 references.
Closest ancestors · 5
Same direction, newest first
- 2025KnowTrace: Bootstrapping Iterative Retrieval-Augmented Generation with Structured Knowledge Tracing26 May
- 2025KET-RAG: A Cost-Efficient Multi-Granular Indexing Framework for Graph-RAG13 Feb
- 2025GORAG: Graph-based Online Retrieval Augmented Generation for Dynamic Few-shot Social Media Text Classification6 Jan
- 2024KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation10 Sep
- 2024Graph Retrieval-Augmented Generation: A Survey15 Aug
Other references · 6
- 2024HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systemscited by 37
- 2024ChunkRAG: Novel LLM-Chunk Filtering Method for RAG Systemscited by 23
- 2025A Survey on Retrieval And Structuring Augmented Generation with Large Language Modelscited by 8
- 2024MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labelscited by 7
- 2025Reference-Aligned Retrieval-Augmented Question Answering over Heterogeneous Proprietary Documentscited by 1
- 2025KRAFT: A Knowledge Graph-Based Framework for Automated Map Conflationcited by 1
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- 2026Graphs RAG at Scale: Beyond Retrieval-Augmented Generation With Labeled Property Graphs and Resource Description Framework for Complex and Unknown Search Spaces
- 2026GraphSteal: Structural Knowledge Stealing from Graph RAG via Traversal Reconstruction
- 2026PAGE-RAG: Provenance-Aware Graph Evidence Promotion for Fixed-Budget Multi-hop Retrieval-Augmented Generation
- 2026FlexStructRAG: Flexible Structure-Aware Multi-Granular Relational Retrieval for RAG
- 2026Breaking the Static Graph: Context-Aware Traversal for Robust Retrieval-Augmented Generation
- 2026Query-Aware Flow Diffusion for Graph-Based RAG with Retrieval Guarantees
- 2026HG-RAG: Hierarchy-Guided Retrieval-Augmented Generation for Structured Knowledge Graphs
- 2026Orion-RAG: Path-Aligned Hybrid Retrieval for Graphless Data