RAGA: Reading-And-Graph-building-Agent for Autonomous Knowledge Graph Construction and Retrieval-Augmented Generation
The article introduces RAGA, a new framework for autonomous knowledge graph construction and retrieval-augmented generation. RAGA addresses limitations in existing methods by integrating a cognitive constraint and a synchronization mechanism for improved retrieval. Preliminary experiments suggest that RAGA enhances both answer and evidence quality compared to traditional approaches.
- ▪RAGA stands for Reading And Graph-building Agent, designed for autonomous knowledge graph construction.
- ▪The framework incorporates a Read-Search-Verify-Construct cognitive constraint to improve the construction process.
- ▪Preliminary experiments indicate that RAGA's fusion retrieval outperforms zero-shot baselines in quality.
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Computer Science > Artificial Intelligence arXiv:2605.17072 (cs) [Submitted on 16 May 2026] Title:RAGA: Reading-And-Graph-building-Agent for Autonomous Knowledge Graph Construction and Retrieval-Augmented Generation Authors:Chengrui Han, Zesheng Cheng View a PDF of the paper titled RAGA: Reading-And-Graph-building-Agent for Autonomous Knowledge Graph Construction and Retrieval-Augmented Generation, by Chengrui Han and 1 other authors View PDF HTML (experimental) 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.
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