mlsanityData to 14 Oct 2026

KG2QA: Knowledge Graph-enhanced Retrieval-augmented Generation for Communication Standards Question Answering

arXiv 2506.07037 ↗First posted 8 Jun 2025cs.CLcited by 1

On the map: Retrieval-augmented generation → Knowledge-graph and structured-knowledge RAG

Abstract (arXiv)

The rapid evolution of communication technologies has led to an explosion of standards, rendering traditional expert-dependent consultation methods inefficient and slow. To address this challenge, we propose KG2QA, a question answering (QA) framework for communication standards that integrates fine-tuned large language models (LLMs) with a domain-specific knowledge graph (KG) via a retrieval-augmented generation (RAG) pipeline. We construct a high-quality dataset of 6,587 QA pairs from ITU-T recommendations and fine-tune Qwen2.5-7B-Instruct, achieving significant performance gains: BLEU-4 increases from 18.86 to 66.90, outperforming both the base model and Llama-3-8B-Instruct. A structured KG containing 13,906 entities and 13,524 relations is built using LLM-assisted triple extraction based on a custom ontology. In our KG-RAG pipeline, the fine-tuned LLMs first retrieves relevant knowledge from KG, enabling more accurate and factually grounded responses. Evaluated by DeepSeek-V3 as a judge, the KG-enhanced system improves performance across five dimensions, with an average score increase of 2.26%, demonstrating superior factual accuracy and relevance. Integrated with Web platform and API, KG2QA delivers an efficient and interactive user experience. Our code and data have been open-sourced https://github.com/luozhongze/KG2QA.

Key facts. Cited by 1 paper 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 19 of its 50 references that are in the map. First posted on arXiv 8 Jun 2025.

Builds on

19 of 50 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization3 Oct

Other references · 18

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  4. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 452
  5. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 354
  6. 2024RAFT: Adapting Language Model to Domain Specific RAGcited by 262
  7. 2023OpenAGI: When LLM Meets Domain Expertscited by 197
  8. 2022Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answeringcited by 127
  9. 2024Data-efficient Fine-tuning for LLM-based Recommendationcited by 104
  10. 2024CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question Answeringcited by 72
  11. 2020Domain-specific Knowledge Graphs: A surveycited by 68
  12. 2023Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Modelscited by 68
6 more
  1. 2021KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answeringcited by 67
  2. 2023Large Language Model Enhanced Multi-Agent Systems for 6G Communicationscited by 53
  3. 2023Exploring Large Language Models for Knowledge Graph Completioncited by 43
  4. 2024From human experts to machines: An LLM supported approach to ontology and knowledge graph constructioncited by 40
  5. 2024NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Datacited by 34
  6. 2024ForPKG: A Framework for Constructing Forestry Policy Knowledge Graph and Application Analysiscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026S-Path-RAG: Semantic-Aware Shortest-Path Retrieval Augmented Generation for Multi-Hop Knowledge Graph Question Answeringcited by 3

Similar, not cited

Close in wording, no citation either way

  1. 2025Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation
  2. 2024Aggregated Knowledge Model: Enhancing Domain-Specific QA with Fine-Tuned and Retrieval-Augmented Generation Models
  3. 2025Knowledge Graph-extended Retrieval Augmented Generation for Question Answering
  4. 2025KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering
  5. 2025Fusing Knowledge and Language: A Comparative Study of Knowledge Graph-Based Question Answering with LLMs
  6. 2026KG2Code: Bridging Knowledge Graphs and Large Language Models via Executable Code for Question Answering
  7. 2026RPO-RAG: Aligning Small LLMs with Relation-aware Preference Optimization for Knowledge Graph Question Answering
  8. 2025BYOKG-RAG: Multi-Strategy Graph Retrieval for Knowledge Graph Question Answering