Structured Linked Data as a Memory Layer for Agent-Orchestrated Retrieval
Andrea Volpini, Elie Raad, Beatrice Gamba, David Riccitelli

TL;DR
This paper demonstrates that using structured linked data and enhanced entity pages significantly improves retrieval accuracy and answer quality in retrieval-augmented generation systems across multiple domains.
Contribution
It introduces an enhanced entity page format with agent instructions and navigational features, showing substantial accuracy improvements over traditional document representations.
Findings
Enhanced entity pages improve accuracy by ~30%.
JSON-LD markup alone offers modest gains.
Rich navigational features yield highest scores.
Abstract
Retrieval-Augmented Generation (RAG) systems typically treat documents as flat text, ignoring the structured metadata and linked relationships that knowledge graphs provide. In this paper, we investigate whether structured linked data, specifically Schema.org markup and dereferenceable entity pages served by a Linked Data Platform, can improve retrieval accuracy and answer quality in both standard and agentic RAG systems. We conduct a controlled experiment across four domains (editorial, legal, travel, e-commerce) using Vertex AI Vector Search 2.0 for retrieval and the Google Agent Development Kit (ADK) for agentic reasoning. Our experimental design tests seven conditions: three document representations (plain HTML, HTML with JSON-LD, and an enhanced agentic-optimized entity page) crossed with two retrieval modes (standard RAG and agentic RAG with multi-hop link traversal), plus an…
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Taxonomy
TopicsSemantic Web and Ontologies · Topic Modeling · Information Retrieval and Search Behavior
