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ARXIV:2603.10700 · RETRIEVAL-AUGMENTED GENERATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.10700RETRIEVAL-AUGMENTED GENERATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Enhancing retrieval accuracy in RAG systems using structured linked data and agentic reasoning.
Opportunity summary
Pain Enhancing retrieval accuracy in RAG systems using structured linked data and agentic reasoning.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Enhancing retrieval accuracy in RAG systems using structured linked data and agentic reasoning. In this paper, we investigate whether structured linked data, specifically Schema.org markup and dereferenceable entity pages served by a Linked Data…
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…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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-Augmented Generation moved forward this cycle; last verified April 2026. Public score 7.0/10.
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Enhancing retrieval accuracy in RAG systems using structured linked data and agentic reasoning.
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10.48550/arXiv.2603.10700Enhancing retrieval accuracy in RAG systems using structured linked data and agentic reasoning.
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.
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Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 7.0
PROBLEM
Enhancing retrieval accuracy in RAG systems using structured linked data and agentic reasoning. 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 a...
METHOD
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 derefere...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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 qu...
WHY NOW
Retrieval-Augmented Generation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Enhancing retrieval accuracy in RAG systems using structured linked data and agentic reasoning. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Retrieval-Augmented Generation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Enhancing retrieval accuracy in RAG systems using structured linked data and agentic reasoning.
Segment
Retrieval-Augmented Generation
Adoption evidence
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Commercial read
7.0/10 public viability
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status
missing
reason
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proof status
unverified
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confidence low
next verification path
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Source missing: Build Passport payload.
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stale
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Build readiness
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passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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Evidence
0 references, 0 sources, 17% evidence coverage.
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Buyer clarity
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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People
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People
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ARTIFACTS
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DEFENSIBILITY
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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