Structured Linked Data as a Memory Layer for Agent-Orchestrated Retrieval

• URL: http://arxiv.org/abs/2603.10700v1
• Date: Wed, 11 Mar 2026 12:12:51 GMT
• Title: Structured Linked Data as a Memory Layer for Agent-Orchestrated Retrieval
• Abstract summary: We investigate whether structured linked data, specifically.org markup and dereferenceable entity pages, can improve retrieval accuracy and answer quality in both standard and agentic RAG systems.<n>We conduct a controlled experiment across four domains (editorial, legal, travel, e-commerce) using Vertex AI Vector Search 2.0 for retrieval.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Is GraphRAG Needed? From Basic RAG to Graph-/Agentic Solutions with Context Optimization [9.99981815735792]
  We introduce a framework for different RAG scenarios evaluation and comparison on semi-structured knowledge bases.<n>We provide implementation for 9 standardized RAG scenarios, and conduct experiments for a comprehensive comparison.
  arXiv  Detail & Related papers  (2026-06-24T10:11:02Z)
• Beyond Relevance: On the Relationship Between Retrieval and RAG Information Coverage [89.58253972744531]
  Retrieval-augmented generation (RAG) systems combine document retrieval with a generative model to address complex information seeking tasks like report generation.<n>We investigate whether upstream retrieval metrics can serve as reliable early indicators of the final generated response's information coverage.<n>Our findings demonstrate strong correlations between coverage-based retrieval metrics and nugget coverage in generated responses at both topic and system levels.
  arXiv  Detail & Related papers  (2026-03-09T18:20:20Z)
• AgentIR: Reasoning-Aware Retrieval for Deep Research Agents [76.29382561831105]
  Deep Research agents generate explicit natural language reasoning before each search call.<n> Reasoning-Aware Retrieval embeds the agent's reasoning trace alongside its query.<n>DR- Synth generates Deep Research retriever training data from standard QA datasets.<n>AgentIR-4B achieves 68% accuracy with the open-weight agent Tongyi-DeepResearch.
  arXiv  Detail & Related papers  (2026-03-04T18:47:26Z)
• TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework [62.66056331998838]
  TeaRAG is a token-efficient agentic RAG framework capable of compressing both retrieval content and reasoning steps.<n>Our reward function evaluates the knowledge sufficiency by a knowledge matching mechanism, while penalizing excessive reasoning steps.
  arXiv  Detail & Related papers  (2025-11-07T16:08:34Z)
• AgentExpt: Automating AI Experiment Design with LLM-based Resource Retrieval Agent [36.65355075707938]
  One key application in AI research is to automate experiment design through agentic and baseline retrieval.<n>We present a comprehensive framework for baseline and dataset recommendation.<n>We develop a reasoning-augmented reranker that exact interaction chains to construct explicit reasoning chains and finetunes a large language model to produce interpretable justifications.
  arXiv  Detail & Related papers  (2025-11-07T01:51:56Z)
• WebGen-Agent: Enhancing Interactive Website Generation with Multi-Level Feedback and Step-Level Reinforcement Learning [51.14454312533818]
  WebGen-Agent is a novel website-generation agent that leverages comprehensive and multi-level visual feedback.<n>We introduce textitStep-GRPO with Screenshot and GUI-agent Feedback to improve the ability of LLMs to act as the reasoning engine of WebGen-Agent.<n>WebGen-Agent increases the accuracy of Claude-3.5-Sonnet from 26.4% to 51.9% and its appearance score from 3.0 to 3.9, outperforming the previous state-of-the-art agent system.
  arXiv  Detail & Related papers  (2025-09-26T17:59:51Z)
• Enhancing Document VQA Models via Retrieval-Augmented Generation [1.6769365072542683]
  Document VQA must cope with documents that span dozens of pages, yet leading systems still rely on very large vision-language models.<n>Retrieval-Augmented Generation (RAG) offers an attractive alternative, first retrieving a concise set of relevant segments before generating answers.
  arXiv  Detail & Related papers  (2025-08-26T12:32:55Z)
• Advancing Retrieval-Augmented Generation for Structured Enterprise and Internal Data [0.0]
  Large Language Models (LLMs) have strong generative capabilities.<n>They are limited by static pretraining, short context windows, and challenges in processing heterogeneous data formats.<n>Conventional Retrieval-Augmented Generation (RAG) frameworks address some of these gaps but often struggle with structured and semi-structured data.<n>This work proposes an advanced RAG framework that combines hybrid retrieval strategies using dense embeddings (all-mpnet-base-v2) and BM25, enhanced by metadata-aware filtering with SpaCy NER and cross-encoder reranking.
  arXiv  Detail & Related papers  (2025-07-16T17:13:06Z)
• QuIM-RAG: Advancing Retrieval-Augmented Generation with Inverted Question Matching for Enhanced QA Performance [1.433758865948252]
  This work presents a novel architecture for building Retrieval-Augmented Generation (RAG) systems.<n>RAG architecture is constructed to generate responses from the target document.<n>We introduce QuIM-RAG, a novel approach for the retrieval mechanism in our system.
  arXiv  Detail & Related papers  (2025-01-06T01:07:59Z)
• Discourse Features Enhance Detection of Document-Level Machine-Generated Content [53.41994768824785]
  Machine-generated content poses challenges such as academic plagiarism and the spread of misinformation.<n>Existing MGC detectors often focus solely on surface-level information, overlooking implicit and structural features.<n>We introduce novel methodologies and datasets to overcome these challenges.
  arXiv  Detail & Related papers  (2024-12-17T08:47:41Z)
• SiReRAG: Indexing Similar and Related Information for Multihop Reasoning [96.60045548116584]
  SiReRAG is a novel RAG indexing approach that explicitly considers both similar and related information.<n>SiReRAG consistently outperforms state-of-the-art indexing methods on three multihop datasets.
  arXiv  Detail & Related papers  (2024-12-09T04:56:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.