面向知识图谱的问答技术研究综述

doi:10.3778/j.issn.1002-8331.2501-0066

摘要/Abstract

摘要： 智能问答是从海量数据中精确、快速获取需求信息的一种关键技术。近年来，智能问答技术发展成果显著，例如，基于问题的信息提取技术、语义理解技术以及向量建模的方法等。然而，随着智能问答技术的迅速发展，人们迫切希望能够对智能问答模型有一个合理的划分方式，以方便不同领域的用户使用。为了合理划分智能问答模型，方便智能问答领域研究者的深度研究，通过对知识图谱问答领域相关文献进行调查，实现了对当前知识图谱问答关键技术的概括，包括实体链接、知识嵌入，并详细介绍了知识图谱问答的相关概念和处理流程。此外，根据方法的不同，将面向知识图谱的问答技术主要分为三大类：基于语义解析方法、基于信息检索方法和基于大语言模型的方法，介绍了其优缺点并分别针对知识图谱问答模型的评价指标进行总结。最后，针对知识图谱问答技术现存的一些问题以及未来发展的方向，提出了一些建议和思考。

关键词: 知识图谱（KG）, 智能问答（QA）, 大数据, 语义解析, 信息检索

Abstract: Intelligent question answering is a key technology to obtain demand information accurately and quickly from massive data. In recent years, intelligent question answering technology has achieved remarkable development, such as problem-based information extraction technology, semantic understanding technology and vector modeling method. However, with the rapid development of intelligent question answering technology, people are eager to have a reasonable division of intelligent question answering model to facilitate the use of users in different fields. In order to divide intelligent question answering model reasonably, it is convenient for researchers in the field of intelligent question answering to conduct in-depth research. Through the investigation of relevant literature in the field of knowledge graph Q&A, this paper summarizes the key technologies of knowledge graph Q&A, including entity linking and knowledge embedding, and introduces the related concepts and processing flow of knowledge graph Q&A in detail. In addition, according to different methods, knowledge graph-oriented question answering techniques are divided into three main categories: semantic parsing, information retrieval and large language model-based methods, and their advantages and disadvantages are introduced and the evaluation indexes of knowledge graph question answering models are summarized respectively. Finally, some suggestions and thoughts are put forward for the existing problems and the future development direction of knowledge graph question-answering technology.

Key words: knowledge graph(KG), questions and answers(QA), big data, semantic analysis, information retrieval

钱慎一, 付博文, 李代祎, 梁瑶瑶. 面向知识图谱的问答技术研究综述[J]. 计算机工程与应用, 2025, 61(23): 1-23.

QIAN Shenyi, FU Bowen, LI Daiyi, LIANG Yaoyao. Review of Question Answering Techniques for Knowledge Graph[J]. Computer Engineering and Applications, 2025, 61(23): 1-23.

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