知识图谱问答研究进展  

Knowledge Graph Question Answering:A Review

作　　者：论兵 王月春 郝晓慧 谷斌 王会勇[3] LUN Bing;WANG Yue-chun;HAO Xiao-hui;GU Bin;WANG Hui-yong(Computer Department,Shijiazhuang Post and Telecommunication Technical College;Talent Assessment Center,China Post Group Corporation;College of Information Science and Engineering,Hebei University of Science and Technology,Shijiazhuang 050000,China)

机构地区：[1]石家庄邮电职业技术学院计算机系 [2]中国邮政集团公司人才测评中心 [3]河北科技大学信息科学与工程学院,河北石家庄050000

出　　处：《软件导刊》2022年第3期226-236,共11页Software Guide

基　　金：河北省高等学校科学技术研究项目(ZD2021048,QN2021315);石家庄市科学技术研究与发展计划项目(211260671)。

摘　　要：知识图谱问答系统以其精准、高效的问答能力,被广泛应用于医疗、金融、电子商务等领域。近年来,随着知识图谱构建技术和深度学习技术的快速发展,知识图谱问答方法源源不断地被提出。以知识图谱问答技术为主线,对知识图谱问答研究进展进行综述。首先,介绍语义解析、信息检索和知识嵌入在内的3种主要知识图谱问答方法;其次,详细阐述知识图谱问答测评任务常用的通用领域和特定领域知识图谱问答数据集;最后,总结知识图谱问答面临的挑战,并对未来研究方向进行展望。Knowledge graph question answering systems(KGQA)has been widely used in many fields such as medical care,finance and e-commerce,because of its precise and efficient ability.Recently,with the rapid development of knowledge graph complete(KGC)and deep learning(DL)technology,KGQA methods have been forward continuously.In accordance with the development of KGQA method,the KGQA research is summarized.First,introduce three KGQA methods including semantic parsing-based,information retrieval-based and knowledge embedding-based methods.Second,the general domain datasets and specific domain datasets in the evaluation task of KGQA commonly used be introduced in detail.Finally,the challenges of KGQA methods and future research directions are summarized.

关 键 词：知识图谱 问答系统 语义解析 信息检索 知识嵌入 神经网络 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]