arXiv Artificial Intelligence

PolyUQuest: Verifiable Structure-Aware Web RAG over Heterogeneous Graphs

PolyUQuest: Verifiable Structure-Aware Web RAG over Heterogeneous Graphs

Quick summary

arXiv:2607.08269v2 Announce Type: replace Abstract: 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 ent

Key takeaways

  • arXiv:2607.08269v2 Announce Type: replace Abstract: 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 ent

Why it matters

The importance of “PolyUQuest: Verifiable Structure-Aware Web RAG over Heterogeneous Graphs” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗