KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911138922364928 |
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| author | Sun, Yushi Sun, Kai Xu, Yifan Ethan Yang, Xiao Dong, Xin Luna Tang, Nan Chen, Lei |
| author_facet | Sun, Yushi Sun, Kai Xu, Yifan Ethan Yang, Xiao Dong, Xin Luna Tang, Nan Chen, Lei |
| contents | Retrieval-Augmented Generation (RAG) mitigates hallucination in Large Language Models (LLMs) by incorporating external data, with Knowledge Graphs (KGs) offering crucial information for question answering. Traditional Knowledge Graph Question Answering (KGQA) methods rely on semantic parsing, which typically retrieves knowledge strictly necessary for answer generation, thus often suffer from low coverage due to rigid schema requirements and semantic ambiguity. We present KERAG, a novel KG-based RAG pipeline that enhances QA coverage by retrieving a broader subgraph likely to contain relevant information. Our retrieval-filtering-summarization approach, combined with fine-tuned LLMs for Chain-of-Thought reasoning on knowledge sub-graphs, reduces noises and improves QA for both simple and complex questions. Experiments demonstrate that KERAG surpasses state-of-the-art solutions by about 7% in quality and exceeds GPT-4o (Tool) by 10-21%. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_04716 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering Sun, Yushi Sun, Kai Xu, Yifan Ethan Yang, Xiao Dong, Xin Luna Tang, Nan Chen, Lei Computation and Language Artificial Intelligence Information Retrieval Retrieval-Augmented Generation (RAG) mitigates hallucination in Large Language Models (LLMs) by incorporating external data, with Knowledge Graphs (KGs) offering crucial information for question answering. Traditional Knowledge Graph Question Answering (KGQA) methods rely on semantic parsing, which typically retrieves knowledge strictly necessary for answer generation, thus often suffer from low coverage due to rigid schema requirements and semantic ambiguity. We present KERAG, a novel KG-based RAG pipeline that enhances QA coverage by retrieving a broader subgraph likely to contain relevant information. Our retrieval-filtering-summarization approach, combined with fine-tuned LLMs for Chain-of-Thought reasoning on knowledge sub-graphs, reduces noises and improves QA for both simple and complex questions. Experiments demonstrate that KERAG surpasses state-of-the-art solutions by about 7% in quality and exceeds GPT-4o (Tool) by 10-21%. |
| title | KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering |
| topic | Computation and Language Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2509.04716 |