面向知识自动化的自动问答研究进展

曾帅; 王帅; 袁勇; 倪晓春; 欧阳永基

doi:10.16383/j.aas.2017.c160667

面向知识自动化的自动问答研究进展

doi: 10.16383/j.aas.2017.c160667

曾帅^1,2, ,,
王帅^1,3,,
袁勇^1,2,,
倪晓春^1,2,,
欧阳永基⁴

1.
中国科学院自动化研究所复杂系统管理与控制国家重点实验室北京 100190
2.
青岛智能产业技术研究院青岛 266109
3.
中国科学院大学北京 100049
4.
解放军61786部队北京 102206

基金项目:

国家自然科学基金 61533019

国家自然科学基金 71402178

国家自然科学基金 61233001

国家自然科学基金 71102117

国家自然科学基金 71472174

国家自然科学基金 71702182

国家自然科学基金 71232006

详细信息

作者简介:
王帅    中国科学院大学博士研究生.主要研究方向为社会计算与平行管理.E-mail:wangshuai2015@ia.ac.cn

袁勇    中国科学院自动化研究所复杂系统管理与控制国家重点实验室副研究员.2008年于山东科技大学获得计算机软件与理论专业博士学位.主要研究方向为商务智能与计算广告学. E-mail: yong.yuan@ia.ac.cn

倪晓春    中国科学院自动化研究所复杂系统管理与控制国家重点实验室工程师.2008年于大连海事大学获得管理科学与工程专业硕士学位.主要研究方向为商务智能与知识自动化. E-mail:xiaochun.ni@ia.ac.cn

欧阳永基    解放军61786部队工程师.2015年于解放军信息工程大学获得计算机软件与理论专业博士学位.主要研究方向为网络安全

通讯作者:
曾帅中国科学院自动化研究所复杂系统管理与控制国家重点实验室助理研究员.2011年于北京邮电大学获得信号与信息处理专业博士学位.主要研究方向为社会计算与策略优化.本文通信作者. E-mail:shuai.zeng@ia.ac.cn

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出版历程
- 收稿日期: 2016-09-18
- 录用日期: 2017-05-31
- 刊出日期: 2017-09-20

Towards Knowledge Automation: A Survey on Question Answering Systems

ZENG Shuai^{1,2
, ,},
WANG Shuai^{1,3
,},
YUAN Yong^{1,2
,},
NI Xiao-Chun^{1,2
,},
OUYANG Yong-Ji⁴

1.
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190
2.
Qingdao Academy of Intelligent Industries, Qingdao 266109
3.
University of Chinese Academy of Sciences, Beijing 100049
4.
Troops 61786 of People's Liberation Army, Beijing 102206

Funds:

National Natural Science Foundation of China 61533019

National Natural Science Foundation of China 71402178

National Natural Science Foundation of China 61233001

National Natural Science Foundation of China 71102117

National Natural Science Foundation of China 71472174

National Natural Science Foundation of China 71702182

National Natural Science Foundation of China 71232006

More Information

Author Bio:
   Ph.D. candidate at the University of Chinese Academy of Sciences. His research interest covers social computing and parallel management.E-mail:

   Associate professor at the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. He received his Ph.D. degree in computer software and theory from Shandong University of Science and Technology in 2008. His research interest covers business intelligence and computational advertising.E-mail:

   Engineer at the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. He received his master degree in management science and engineering at Dalian Maritime University in 2008. His research interest covers business intelligence and knowledge automation .E-mail:

   Engineer at the Troops 61786 of People$'$s Liberation Army. He received his Ph.D. degree in computer software and theory from PLA Information Engineering University in 2015. His main research interest is network security

Corresponding author: ZENG Shuai Assistant professor at the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. She received her Ph.D. degree in signal and information processing from Beijing University of Post & Telecommunication in 2011. Her research interest covers social computing and strategy optimaization. Corresponding author of this paper.E-mail:shuai.zeng@ia.ac.cn

摘要

摘要: 将自动问答系统从基于文本关键词的层面，提升到基于知识的层面，实现个性化、智能化的知识机器人，已成为自动问答系统未来的发展趋势与目标.本文从知识管理的角度出发，分析和总结自动问答领域的最新研究成果.按照知识表示方法，对代表性自动问答系统及关键问题进行了描述和分析；并对主流的英文、中文自动问答应用和主要评测方法进行了介绍.
- 自动问答系统 /
- 知识机器人 /
- 知识自动化 /
- 知识工程
Abstract: Question answering systems are evolving from the text keywords level to the next knowledge-based level, and thus realizing personalized and intelligent knowledge robots has become the development trend and goal for the future question answering systems. From the viewpoint of knowledge management, this paper analyzes and summarizes the latest research findings in the field of automatic question answering. According to the knowledge representation methods, this paper surveys several representative question answering systems and analyzes their key technologies. Also investigated are some mainstream English and Chinese-language-based question answering applications and the commonly used evaluation methods.
- Question answering system /
- knowledge robots /
- knowledge automation /
- knowledge engineering
注释:

1) 本文责任编委王飞跃

HTML全文

图 1 自动问答与知识管理生命周期的关联关系

Fig. 1 The relationship between QA & knowledge management life cycle

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图 2 基本架构

Fig. 2 System architecture

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图 3 问题的逻辑表示

Fig. 3 The logic form of an example question

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图 4 图结构的问答规则^[40]

Fig. 4 An example QA rule represented as a graph{^[40]

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图 5 概念Binding-event的框架表示^[44]

Fig. 5 The frame of Binding-event^[44]

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图 6 语义网络示例^[52]

Fig. 6 An example of semantic networks^[52]

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图 7 问题查询示例^[52]

Fig. 7 An example of logic form query^[52]

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图 8 基于本体的问答系统: Pythia^[54]

Fig. 8 An example QA system based on ontology: Pythia^[54]

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图 9 卷积神经网络模型^[59]

Fig. 9 An example CNN model^[59]

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图 10 注意力模型^[60]

Fig. 10 An example attention model^[60]

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表 1 典型英文自动问答系统

Table 1 A list of English QA systems

问答系统	问题类型	数据源	答案形式	相关技术
START	What, Who, When等开头的事实型或者定义型问题	START KB、Internet Public Library	一句话或者一段文字	自然语言注释(Natural language annotations)、句子级别的自然语言处理(Sentence-level NLP)
AnswerBus	开放领域问答系统	互联网	按照相关程度返回若干个可能的候选答案语句	命名实体抽取(Named entities extraction)
Evi	开放领域问答系统	自有结构化知识库(Structured knowledge base), Yelp和第三方网站的数据和API	类似人类语言风格的简明回答	知识表示
AskJeeves	开放领域问答系统	自有问答数据库、互联网	文本、文档链接以及内容摘要	自然语言检索技术(NLP)、人工操作目录索引
Wolfram Alpha	开放领域问答系统	内置的结构化知识库	包含答案信息的各种数据和图表	计算知识引擎(Computational knowledge)
Watson	开放领域问答系统	定义了自身的知识框架, 并从海量结构化和半结构化资料中抽取知识构建知识体系	针对用户提问的精准回答	统计机器学习、句法分析、主题分析、信息抽取、知识库集成和知识推理

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表 2 典型中文自动问答系统

Table 2 A list of Chinese QA systems

问答系统	问题类型	数据源	答案形式	相关技术
微软小冰	日常聊天伴侣	海量网民聊天语料库	拟人化回答	情感计算、自主知识学习、意图对接对话引擎
京东JIMI	电商售前、售后咨询	自有问答库	文本	深度神经网络、意图识别、命名实体识别
小i机器人	业务咨询	语言知识库以及业务知识库	文本	知识表示、本体理论、分领域的语义网络
度秘	生活服务类咨询	互联网	服务推荐(如餐厅、影院)	全网数据挖掘和聚合
阿里小蜜	导购咨询	自有语料库	文本、语音、网页链接等	知识图谱、语义理解、个性化推荐、深度学习

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