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基于表示学习的知识库问答研究进展与展望

刘康 张元哲 纪国良 来斯惟 赵军

刘康, 张元哲, 纪国良, 来斯惟, 赵军. 基于表示学习的知识库问答研究进展与展望. 自动化学报, 2016, 42(6): 807-818. doi: 10.16383/j.aas.2016.c150674
引用本文: 刘康, 张元哲, 纪国良, 来斯惟, 赵军. 基于表示学习的知识库问答研究进展与展望. 自动化学报, 2016, 42(6): 807-818. doi: 10.16383/j.aas.2016.c150674
LIU Kang, ZHANG Yuan-Zhe, JI Guo-Liang, LAI Si-Wei, ZHAO Jun. Representation Learning for Question Answering over Knowledge Base: An Overview. ACTA AUTOMATICA SINICA, 2016, 42(6): 807-818. doi: 10.16383/j.aas.2016.c150674
Citation: LIU Kang, ZHANG Yuan-Zhe, JI Guo-Liang, LAI Si-Wei, ZHAO Jun. Representation Learning for Question Answering over Knowledge Base: An Overview. ACTA AUTOMATICA SINICA, 2016, 42(6): 807-818. doi: 10.16383/j.aas.2016.c150674

基于表示学习的知识库问答研究进展与展望

doi: 10.16383/j.aas.2016.c150674
基金项目: 

国家自然科学基金 61533018

国家重点基础研究发展计划(973计划) 2014CB340503

详细信息
    作者简介:

    张元哲 中国科学院自动化研究所博士研究生. 主要研究方向为问答系统和自然语言处理. E-mail: yzzhang@nlpr.ia.ac.cn

    纪国良 中国科学院自动化研究所博士研究生. 主要研究方向为知识工程和自然语言处理. E-mail: guoliang.ji@nlpr.ia.ac.cn

    来斯惟 中国科学院自动化研究所博士研究生. 主要研究方向为表示学习和自然语言处理. E-mail: swlai@nlpr.ia.ac.cn

    赵军 中国科学院自动化研究所研究员. 主要研究方向为信息检索, 信息提取, 网络挖掘, 问答系统. E-mail: jzhao@nlpr.ia.ac.cn

    通讯作者:

    刘康 中国科学院自动化研究所副研究员. 主要研究方向为问答系统, 观点挖掘, 自然语言处理. 本文通信作者. E-mail: kliu@nlpr.ia.ac.cn

Representation Learning for Question Answering over Knowledge Base: An Overview

Funds: 

National Natural Science Foundation of China 61533018

National Basic Research Program of China (973 Program) 2014CB340503

More Information
    Author Bio:

    ZHANG Yuan-Zhe Ph. D. candidate at the Institute of Automation, Chinese Academy of Sciences. His research interest covers question answering and natural language processing

    JI Guo-Liang Ph. D. candidate at the Institute of Automation, Chinese Academy of Sciences. His research interest covers knowledge engineering and natural language processing

    LAI Si-Wei Ph. D. candidate at the Institute of Automation, Chinese Academy of Sciences. His research interest covers representation learning and natural language processing

    ZHAO Jun Professor at the Institute of Automation, Chinese Academy of Sciences. His research interest covers information retrieval, information extraction, web mining and question answering

    Corresponding author: LIU Kang Associate professor at the Institute of Automation, Chinese Academy of Sciences. His research interest covers question answering, opinion mining, and natural language processing. Corresponding author of this paper
  • 摘要: 面向知识库的问答(Question answering over knowledge base, KBQA)是问答系统的重要组成. 近些年, 随着以深度学习为代表的表示学习技术在多个领域的成功应用, 许多研究者开始着手研究基于表示学习的知识库问答技术. 其基本假设是把知识库问答看做是一个语义匹配的过程. 通过表示学习知识库以及用户问题的语义表示, 将知识库中的实体、关系以及问句文本转换为一个低维语义空间中的数值向量, 在此基础上, 利用数值计算, 直接匹配与用户问句语义最相似的答案. 从目前的结果看, 基于表示学习的知识库问答系统在性能上已经超过传统知识库问答方法. 本文将对现有基于表示学习的知识库问答的研究进展进行综述, 包括知识库表示学习和问句(文本)表示学习的代表性工作, 同时对于其中存在难点以及仍存在的研究问题进行分析和讨论.
  • 图  1  知识库问答过程

    Fig.  1  The process of KBQA

    图  2  基于表示学习的知识库问答方法与传统方法的性能比较

    Fig.  2  The comparisons between representation learning based KBQA and traditional KBQA

    图  3  基于表示学习的知识库问答示意图

    Fig.  3  Representation learning based KBQA

    图  4  RESCAL 系统原理[15

    Fig.  4  RESCAL system architecture[15

    图  5  TransE、TransH 和TransR[24-26]

    Fig.  5  TransE、TransH 和TransR[24-26]

    图  6  递归神经网络结构图

    Fig.  6  Recursive neural network architecture

    图  7  循环神经网络模型结构图

    Fig.  7  Recurrent neural network architecture

    图  8  卷积神经网络模型结构图[46]

    Fig.  8  Convolutional neural network architecture[46]

    图  9  Subgraph embedding 模型[11]

    Fig.  9  Subgraph embedding model[11]

    图  10  处理问句的CNN 模型[51]

    Fig.  10  CNN model used to process question[51]

    图  11  Multi-column CNN 模型[12

    Fig.  11  Multi-column CNN model[12

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  • 收稿日期:  2015-11-02
  • 录用日期:  2016-05-03
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