RAGA: Reading-And-Graph-building-Agent for Autonomous Knowledge Graph Construction and Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2605.17072v1
• Date: Sat, 16 May 2026 16:42:50 GMT
• Title: RAGA: Reading-And-Graph-building-Agent for Autonomous Knowledge Graph Construction and Retrieval-Augmented Generation
• Abstract summary: RAGA (Reading And Graph-building Agent) is an autonomous knowledge graph construction and retrieval framework.<n>It provides full KG lifecycle CRUD operations and embeds a Read-Search-Verify-Verify cognitive constraint into a ReAct tool loop.<n>Preliminary experiments on a subset of the QASPER scientific QA dataset indicate that RAGA's fusion retrieval outperforms zero-shot baselines.
• Score: 1.7016397531234393
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Existing LLM-driven knowledge graph (KG) construction methods predominantly employ stateless batch processing pipelines, exhibiting structural deficiencies in cross-chunk semantic relation capture, entity disambiguation, and construction process interpretability. These limitations undermine KG quality, retrieval precision, and deployment trust in high-stakes domains. We propose RAGA (Reading And Graph-building Agent), an LLM-based autonomous KG construction and retrieval fusion framework. RAGA provides an atomic toolset supporting full KG lifecycle CRUD operations and embeds a Read-Search-Verify-Construct cognitive constraint into a ReAct tool loop. A KG-vector synchronization mechanism enables hybrid symbolic-vector retrieval, while evidence-anchored verification links every knowledge entry to its source text for auditable provenance. Preliminary experiments on a subset of the QASPER scientific QA dataset indicate that RAGA's fusion retrieval outperforms zero-shot baselines, with KG integration providing measurable gains in both answer and evidence quality. The framework design and experimental baseline serve as a reference for agent-driven autonomous KG construction.

Related papers

• KG2Code: Bridging Knowledge Graphs and Large Language Models via Executable Code for Question Answering [64.63418702318029]
  KG2Code is a novel approach that transforms knowledge graphs into a code-based representation.<n> KG2Code-QA is introduced as a KGQA framework that formulates KGQA as a code generation task.
  arXiv  Detail & Related papers  (2026-06-26T11:27:55Z)
• BLINKG: A Benchmark for LLM-Integrated Knowledge Graph Generation [3.038521817582494]
  Large Language Models (LLMs) can be used to automate the Generating Knowledge Graphs (KGs) process.<n>This paper proposes a benchmark designed to evaluate the mapping capabilities of LLMs in constructing KGs from heterogeneous data sources.
  arXiv  Detail & Related papers  (2026-05-19T08:15:53Z)
• SPARK: Self-Play with Asymmetric Reward from Knowledge Graphs [2.1636750837987195]
  Self-play reinforcement learning has shown strong performance in domains with formally verifiable structure.<n>We propose SPARK, a framework that automatically constructs a unified knowledge graph (KG) from multi-document scientific literature.<n>A single small vision-language model (sVLM) alternates between Proposer and Solver roles under information asymmetry against a fixed KG.<n>Results show that SPARK consistently outperforms flat-corpus-based self-play baselines.
  arXiv  Detail & Related papers  (2026-05-07T00:51:36Z)
• Can Knowledge-Graph-based Retrieval Augmented Generation Really Retrieve What You Need? [57.28763506780752]
  GraphFlow is a framework that efficiently retrieves accurate and diverse knowledge required for real-world queries from text-rich KGs.<n>It outperforms strong KG-RAG baselines, including GPT-4o, by 10% on average in hit rate and recall.<n>It also shows strong generalization to unseen KGs, demonstrating its effectiveness and robustness.
  arXiv  Detail & Related papers  (2025-10-18T17:06:49Z)
• Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enriching [61.824094419641575]
  Large Language Models (LLMs) struggle with hallucinations and factual errors in knowledge-intensive scenarios like knowledge graph question answering (KGQA)<n>We attribute this to the semantic gap between structured knowledge graphs (KGs) and unstructured queries, caused by inherent differences in their focuses and structures.<n>Existing methods usually employ resource-intensive, non-scalable reasoning on vanilla KGs, but overlook this gap.<n>We propose a flexible framework, Enrich-on-Graph (EoG), which leverages LLMs' prior knowledge to enrich KGs, bridge the semantic gap between graphs and queries.
  arXiv  Detail & Related papers  (2025-09-25T06:48:52Z)
• Towards Open-World Retrieval-Augmented Generation on Knowledge Graph: A Multi-Agent Collaboration Framework [21.896955284099334]
  Large Language Models (LLMs) have demonstrated strong capabilities in language understanding and reasoning.<n>Retrieval-Augmented Generation (RAG) addresses this limitation by incorporating external knowledge sources.<n>We propose AnchorRAG, a novel multi-agent collaboration framework for open-world RAG without the predefined anchor entities.
  arXiv  Detail & Related papers  (2025-09-01T08:26:12Z)
• KG-CQR: Leveraging Structured Relation Representations in Knowledge Graphs for Contextual Query Retrieval [5.263064605350636]
  We propose KG-CQR, a novel framework for Contextual Query Retrieval (CQR)<n> KG-CQR focuses on query enrichment through structured relation representations, extracting and completing relevant KG subgraphs to generate semantically rich query contexts.<n> Experimental results on RAGBench and MultiHop-RAG datasets demonstrate KG-CQR's superior performance, achieving a 4-6% improvement in mAP and a 2-3% improvement in Recall@25 over strong baseline models.
  arXiv  Detail & Related papers  (2025-08-28T04:37:15Z)
• KG-Infused RAG: Augmenting Corpus-Based RAG with External Knowledge Graphs [58.12674907593879]
  KG-Infused RAG is a framework that incorporates pre-existing large-scale knowledge graphs into RAG.<n> KG-Infused RAG directly performs spreading activation over external KGs to retrieve relevant structured knowledge.<n>Experiments show that KG-Infused RAG consistently outperforms vanilla RAG.
  arXiv  Detail & Related papers  (2025-06-11T09:20:02Z)
• Ontology-grounded Automatic Knowledge Graph Construction by LLM under Wikidata schema [60.42231674887294]
  We propose an ontology-grounded approach to Knowledge Graph (KG) construction using Large Language Models (LLMs) on a knowledge base.<n>We ground generation of KG with the authored ontology based on extracted relations to ensure consistency and interpretability.<n>Our work presents a promising direction for scalable KG construction pipeline with minimal human intervention, that yields high quality and human-interpretable KGs.
  arXiv  Detail & Related papers  (2024-12-30T13:36:05Z)
• Distill-SynthKG: Distilling Knowledge Graph Synthesis Workflow for Improved Coverage and Efficiency [59.6772484292295]
  Knowledge graphs (KGs) generated by large language models (LLMs) are increasingly valuable for Retrieval-Augmented Generation (RAG) applications. Existing KG extraction methods rely on prompt-based approaches, which are inefficient for processing large-scale corpora. We propose SynthKG, a multi-step, document-level synthesis KG workflow based on LLMs. We also design a novel graph-based retrieval framework for RAG.
  arXiv  Detail & Related papers  (2024-10-22T00:47:54Z)

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.