# PolyUQuest: Verifiable Structure-Aware Web RAG over Heterogeneous Graphs

arXiv: https://arxiv.org/abs/2607.08269 · HTML: https://mlsanity.com/paper/2607.08269/

## 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.

## Placed in

- Retrieval-augmented generation / [Knowledge-graph and structured-knowledge RAG](https://mlsanity.com/rag/knowledge-graph-and-structured-knowledge-rag/)

## Builds on (most cited first, 11 of 11)

- [Graph Retrieval-Augmented Generation: A Survey](https://mlsanity.com/paper/2408.08921/) (2024)
- [KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation](https://mlsanity.com/paper/2409.13731/) (2024)
- [HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems](https://mlsanity.com/paper/2411.02959/) (2024)
- [KET-RAG: A Cost-Efficient Multi-Granular Indexing Framework for Graph-RAG](https://mlsanity.com/paper/2502.09304/) (2025)
- [ChunkRAG: Novel LLM-Chunk Filtering Method for RAG Systems](https://mlsanity.com/paper/2410.19572/) (2024)
- [KnowTrace: Bootstrapping Iterative Retrieval-Augmented Generation with Structured Knowledge Tracing](https://mlsanity.com/paper/2505.20245/) (2025)
- [A Survey on Retrieval And Structuring Augmented Generation with Large Language Models](https://mlsanity.com/paper/2509.10697/) (2025)
- [MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels](https://mlsanity.com/paper/2405.07526/) (2024)
- [GORAG: Graph-based Online Retrieval Augmented Generation for Dynamic Few-shot Social Media Text Classification](https://mlsanity.com/paper/2501.02844/) (2025)
- [Reference-Aligned Retrieval-Augmented Question Answering over Heterogeneous Proprietary Documents](https://mlsanity.com/paper/2502.19596/) (2025)
- [KRAFT: A Knowledge Graph-Based Framework for Automated Map Conflation](https://mlsanity.com/paper/2509.04684/) (2025)

## 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.