# KAQG: A Knowledge-Graph-Enhanced RAG for Difficulty-Controlled Question Generation

arXiv: https://arxiv.org/abs/2505.07618 · HTML: https://mlsanity.com/paper/2505.07618/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 16 of its 46 references that are in the map.
- First posted on arXiv 12 May 2025.

## Placed in

- Retrieval-augmented generation / [Knowledge-graph and structured-knowledge RAG](https://mlsanity.com/rag/knowledge-graph-and-structured-knowledge-rag/)

## Builds on (most cited first, 16 of 16)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [LightRAG: Simple and Fast Retrieval-Augmented Generation](https://mlsanity.com/paper/2410.05779/) (2024)
- [HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models](https://mlsanity.com/paper/2405.14831/) (2024)
- [Retrieval-Augmented Generation with Graphs (GraphRAG)](https://mlsanity.com/paper/2501.00309/) (2024)
- [GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning](https://mlsanity.com/paper/2405.20139/) (2024)
- [Reducing hallucination in structured outputs via Retrieval-Augmented Generation](https://mlsanity.com/paper/2404.08189/) (2024)
- [GRAG: Graph Retrieval-Augmented Generation](https://mlsanity.com/paper/2405.16506/) (2024)
- [Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation](https://mlsanity.com/paper/2407.10805/) (2024)
- [A Comprehensive Survey on Automatic Knowledge Graph Construction](https://mlsanity.com/paper/2302.05019/) (2023)
- [Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation](https://mlsanity.com/paper/2305.18846/) (2023)
- [DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literature](https://mlsanity.com/paper/2405.04819/) (2024)
- [CypherBench: Towards Precise Retrieval over Full-scale Modern Knowledge Graphs in the LLM Era](https://mlsanity.com/paper/2412.18702/) (2024)
- [Cross-Data Knowledge Graph Construction for LLM-enabled Educational Question-Answering System: A Case Study at HCMUT](https://mlsanity.com/paper/2404.09296/) (2024)
- [Construction of Knowledge Graphs: State and Challenges](https://mlsanity.com/paper/2302.11509/) (2023)
- [Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning: A Comparative Study](https://mlsanity.com/paper/2404.11792/) (2024)

## Built on by (most cited first, 2 of 2)

- [A Multi-Agent Framework for Feature-Constrained Difficulty Control in Reading Comprehension Item Generation](https://mlsanity.com/paper/2605.19316/) (2026)
- [TeachMateGPT: A Multi-Agent Knowledge-Grounded Framework for Pedagogical Assessment Generation from Science Curriculum Materials](https://mlsanity.com/paper/2608.13708/) (2026)

## Abstract (arXiv)

This study introduces Knowledge Augmented Question Generation (KAQG), an educational assessment framework that integrates Item Response Theory, abbreviated as IRT, Bloom's Taxonomy, and knowledge graphs into a multi-agent Retrieval-Augmented Generation (RAG) system. The proposed approach overcomes limitations of existing methods by enabling fine-grained control over item difficulty, psychometric calibration, and cognitive alignment. It employs multi-graph isolation to preserve domain-specific semantics and leverages a distributed agent architecture coordinated through Data Distribution Service, abbreviated as DDS, for scalable and fault-tolerant operations. Each agent specializes in tasks such as retrieval, generation, or evaluation, forming a modular and traceable pipeline. Distinctively, the framework encodes semantic hierarchies, PageRank-based concept weighting, and assessment-theory parameters directly into the generation process, ensuring that questions are both contextually grounded and cognitively calibrated. Deployed at Taiwan's National Institute of Environmental Research, the system has demonstrated practical value by reducing manual workload, improving reliability and validity, and supporting both adaptive and standardized assessments. By integrating psychometric theory with AI-driven retrieval and generation, this work establishes a scalable and cognitively aligned solution for education and professional certification.