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SCIENCE CHINA Information Sciences, Volume 60, Issue 11: 110103(2017) https://doi.org/10.1007/s11432-017-9136-x

Topic enhanced deep structured semantic models for knowledge base question answering

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  • ReceivedMar 24, 2017
  • AcceptedJun 20, 2017
  • PublishedSep 21, 2017

Abstract

Knowledge Base Question Answering (KBQA) is a hot research topic in natural language processing (NLP). The most challenging problem in KBQA is how to understand the semantic information of natural language questions and how to bridge the semantic gap between the natural language questions and the structured fact triples in knowledge base. This paper focuses on simple questions which can be answered by a single fact triple in knowledge base. We propose a topic enhanced deep structured semantic model for KBQA. The proposed method considers the task of KBQA as a matching problem between questions and the subjects and predicates in knowledge base. And the proposed model consists of two stages to match the subjects and predicates, respectively. In the first stage, we propose a Convolutional based Topic Entity Extraction Model (CTEEM) to extract topic entities mentioned in questions. With the extracted entities, we can retrieve the relevant candidate fact triples from knowledge base and obviously decrease the amount of noising candidates. In the second stage, we employ Deep Structured Semantic Models (DSSMs) to compute the semantic relevant score between questions and predicates in the candidates. And we combine the semantic level and the lexical level scores to rank the candidates. We evaluate the proposed method on KBQA dataset released by NLPCC-ICCPOL 2016. The experimental results show that our proposed method achieves the third place among the 21 submitted systems. Furthermore, we also extend the DSSM by using BiLSTM and integrate a convolutional structure on the top of BiLSTM layers. Our experimental results show that the extension models can further improve the performance.


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. 61573163, 71571084), Fundamental Research Funds for the Central Universities (Grant No. CCNU16A02024), and Wuhan Youth Science and Technology Plan.


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  • Figure 1

    (Color online) The overview of the KBQA system framework.

  • Figure 2

    (Color online) The architecture of the CTEEM for an example question.

  • Figure 3

    (Color online) The architecture of the CDSSM used in this paper.

  • Figure 4

    (Color online) The architecture of BDSSM.

  • Figure 5

    (Color online) An example of the lexical level matching.

  • Figure 6

    (Color online) The average $F_1$ vs. $\omega$ curves of the three semantic matching methods.

  • Table 1   Topic entities extracted by the CTEEM
    Question Topic entity
    你$|$知道$|$雷锋$|$日记$|-|$拼音$|$版$|$的$|$副标题$|$是$|$什么$|$吗$|$? 雷锋日记–拼音版
    你$|$知道$|$倭$|$叉$|$角$|$羚$|$这种$|$动物$|$是$|$什么$|$纲$|$的$|$吗$|$? 倭叉角羚
    你$|$知道$|$拼搏$|$奥运$|$连连看$|$占$|$多$|$大$|$的$|$内存$|$吗$|$? 拼搏奥运连连看
    奥图$|$码$|$pv$|$3225$|$这款$|$产品$|$是$|$干什么$|$用$|$的$|$啊$|$? 奥图码 pv 3225
    恒大$|$金碧$|$天下$|$是$|$什么$|$样子$|$的$|$房子$|$啊$|$? 恒大金碧天下
    上海$|$假日$|$之$|$星$|$酒店$|$($|$长宁店$|$)$|$在$|$哪$|$啊 $|$? 上海假日之星酒店
    你$|$知道$|$资产$|$预计$|$未来$|$现金流量$|$是$|$什么$|$意思$|$吗$|$? 资产预计未来现金流量
  • Table 2   The comparison of the candidates obtained by using the extracted topic entity and n-gram method
    CTEEM method n-gram method
    noalign
    Total number 6 3000
    Candidates 飞廉状风毛菊$|||$别名$|||$飞廉状风毛菊 于中国$|||$别名$|||$于中国
    飞廉状风毛菊$|||$中文名$|||$飞廉状风毛菊 于中国$|||$中文名$|||$于中国
    飞廉状风毛菊$|||$门$|||$被子植物门 于中国$|||$国籍$|||$中国
    飞廉状风毛菊$|||$国内分布$|||$陕西省, 甘肃省, 四川省 于中国$|||$职业$|||$高级工程师
    飞廉状风毛菊$|||$生境$|||$疏林中 于中国$|||$毕业院校$|||$吉林大学
    飞廉状风毛菊$|||$栽培$|||$非人工引种栽培 飞廉状风毛菊$|||$别名$|||$飞廉状风毛菊
    飞廉状风毛菊$|||$中文名$|||$飞廉状风毛菊
    飞廉状风毛菊$|||$门$|||$被子植物门
    飞廉状风毛菊$|||$国内分布$|||$陕西省, 甘肃省, 四川省
    飞廉状风毛菊$|||$生境$|||$疏林中
    飞廉状风毛菊$|||$栽培$|||$非人工引种栽培
    分布(汉语词汇)$|||$别名$|||$分布
    分布(汉语词汇)$|||$中文名$|||$分布
    $\cdots~\cdots$
  • Table 3   The experimental results on the test dataset
    System $\mbox{Average}~F_1~(%)$
    Baseline system (CDSSM) 52.47
    Lai et al., 2016[14] 82.47
    Compared methods Yang et al., 2016[16] 81.59
    Wang et al., 2016[15] 79.14
    Xie et al., 2016[17] 79.57
    CTEEM+LMS 74.62
    CTEEM+CDSSM 75.74
    CTEEM+BDSSM 74.22
    CTEEM+BCDSSM 76.18
    Ours CTEEM+Combined-DSSM 77.89
    CTEEM+CDSSM+LMS 81.89
    CTEEM+BDSSM+LMS 81.63
    CTEEM+BCDSSM+LMS 82.21
    CTEEM+Combined-DSSM+LMS 82.43

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