自动从杂乱文档构建知识图谱，提升大模型问答能力。
OntoRAG: Enhancing Question-Answering through Automated Ontology Derivation from Unstructured Knowledge Bases
- 用爬虫、解析和提取技术把无序文档转成可查询的本体
- 在电气继电器数据上，问答全面性比向量RAG高85%、比GraphRAG高75%
- 适合需要快速构建领域知识库的工程与研究场景
本体对结构化知识库以增强由大语言模型（LLMs）驱动的问答系统至关重要。然而，传统本体构建依赖领域专家手动操作，耗时长、易出错，难以应对大规模动态知识领域。本文提出OntoRAG，一个自动化流程，从非结构化知识库中衍生本体，聚焦于电气继电器文档。该流程整合网页抓取、PDF解析、混合分块、信息抽取、知识图谱构建与本体生成等技术，将非结构化数据转化为可查询的本体。通过结合大语言模型与图方法，OntoRAG提升了全局理解能力，在全面性与多样性上优于传统检索增强生成（RAG）与GraphRAG。实验表明，OntoRAG在全面性上相较向量RAG赢得85%、相较GraphRAG最佳配置赢得75%。该工作解决了本体自动构建的关键挑战，推动语义网愿景实现。
原文摘要 · Abstract (English)
Ontologies are pivotal for structuring knowledge bases to enhance question answering (QA) systems powered by Large Language Models (LLMs). However, traditional ontology creation relies on manual efforts by domain experts, a process that is time intensive, error prone, and impractical for large, dynamic knowledge domains. This paper introduces OntoRAG, an automated pipeline designed to derive ontologies from unstructured knowledge bases, with a focus on electrical relay documents. OntoRAG integrates advanced techniques, including web scraping, PDF parsing, hybrid chunking, information extraction, knowledge graph construction, and ontology creation, to transform unstructured data into a queryable ontology. By leveraging LLMs and graph based methods, OntoRAG enhances global sensemaking capabilities, outperforming conventional Retrieval Augmented Generation (RAG) and GraphRAG approaches in comprehensiveness and diversity. Experimental results demonstrate OntoRAGs effectiveness, achieving a comprehensiveness win rate of 85% against vector RAG and 75% against GraphRAGs best configuration. This work addresses the critical challenge of automating ontology creation, advancing the vision of the semantic web.
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