🤖 AI Summary
This study addresses the limitations of traditional Retrieval-Augmented Generation (RAG) systems, which often neglect structured metadata and entity relationships within documents, thereby constraining retrieval efficacy and answer quality. To overcome this, the authors propose an agent-optimized enhanced entity page format that leverages linked data—specifically Schema.org annotations and dereferenceable entity pages—and integrates llms.txt instructions, navigational pathways, and neural search capabilities. An Enhanced+ variant is further introduced to strengthen inter-entity linking and navigation support. The system is implemented using Vertex AI Vector Search 2.0 and the Google Agent Development Kit, combining JSON-LD, HTML, and linked data platform technologies. Experimental results demonstrate a 29.6% accuracy improvement over standard RAG and a 29.8% gain in full agent workflows, with the Enhanced+ variant achieving the best performance (accuracy: 4.85/5; completeness: 4.55/5).
📝 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 Enhanced+ condition that adds rich navigational affordances and entity interlinking. Our results reveal that while JSON-LD markup alone provides only modest improvements, our enhanced entity page format, incorporating llms.txt-style agent instructions, breadcrumbs, and neural search capabilities, achieves substantial gains: +29.6% accuracy improvement for standard RAG and +29.8% for the full agentic pipeline. The Enhanced+ variant, with richer navigational affordances, achieves the highest absolute scores (accuracy: 4.85/5, completeness: 4.55/5), though the incremental gain over the base enhanced format is not statistically significant. We release our dataset, evaluation framework, and enhanced entity page templates to support reproducibility.