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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (11): 79-92    DOI: 10.11925/infotech.2096-3467.2022.0185
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Knowledge Modeling and Association Q&A for Policy Texts
Hua Bin1,2,Kang Yue1(),Fan Linhao2
1School of Management Science and Engineering, Tianjin University of Finance and Economics, Tianjin 300222, China
2School of Science and Technology, Tianjin University of Finance and Economics, Tianjin 300222, China
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[Objective] This paper develops a smart question-answering model for association policy based on cognitive semantic knowledge understanding, aiming to improve the government services. [Methods] First, we established a model based on policy connotation to express policy knowledge. Then, we introduced the attention mechanism for question words and classified policy issues combining the improved ERNIE + CNN model. Third, we used the semantic role labeling IDCNN + CRF model and cognitive computing method to obtain the semantics and pragmatic knowledge. Finally, based on knowledge fusion and semantic retrieval, we utilized knowledge aggregation technology to generate relevant answers. We also adopted the BERT semantic similarity calculation and knowledge unit measurement to evaluate the quality of answers. [Results] The accuracy of problem classification reached 90.76%, which was 18.81% and 5.05% higher than those of the original BERT and ERNIE models. The precision of problem knowledge acquisition reached 95.88%, and the accuracy of the answer quality reached 93.75%. The semantic similarity of the answers was 0.88, while the knowledge consistency was 0.96. [Limitations] The performance of our model is limited by the integrity of the domain knowledge system, while the answers’ relevance relies on the accuracy of policy knowledge extraction. [Conclusions] Based on the deconstruction of policy contents and scientific knowledge representation, the proposed method can generate answers for questions on different policy contents.

Key wordsIntelligent Question and Answering      Text Mining      E-Government      Policy Knowledge Model      Knowledge Graph      Knowledge Aggregation     
Received: 07 March 2022      Published: 13 January 2023
ZTFLH:  TP391  
Corresponding Authors: Kang Yue     E-mail:

Cite this article:

Hua Bin,Kang Yue,Fan Linhao. Knowledge Modeling and Association Q&A for Policy Texts. Data Analysis and Knowledge Discovery, 2022, 6(11): 79-92.

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The Framework of Policy Association Question Answering
The Construction of the Policy Knowledge Model
The Structure of QAM-ERNIE-CNN
关系名称 语义关系描述
has_content (具有政策内容) 政策与政策内容之间的关系
has_subject(具有政策主体) 政策与政策主体之间的关系
has_object(具有政策客体) 政策内容与政策客体之间的关系
has_event(具有事件) 政策内容与事件之间的关系
has_action(具有动作) 政策内容与动作之间的关系
has_central(具有国家级) 政策主体与中央政府之间的关系
has_local(具有地方级) 政策主体与地方政府之间的关系
has_park(具有园区级) 政策主体与产业园区之间的关系
Description of the Semantic Relationship Between Concepts
The Result of Optimal Threshold Knowledge Fusion
The Knowledge Graph of One Policy
问题类别 训练集数量 验证集数量 测试集数量
事实类 8 466 2 117 184
列表类 2 135 534 296
判断类 64 16 160
方法类 776 194 246
计数类 4 061 1 015 39
原因类 52 13 18
选择类 33 8 9
总数 15 587 3 897 952
The Statistics of Problems Classification Data
模型 Model(no QAM) Model(QAM)
准确率/% 损失 准确率/% 损失
BERT 71.95 1.10 77.94 0.85
BERT-CNN 76.16 0.98 80.36 0.90
BERT-RNN 68.80 1.40 69.85 1.30
BERT-RCNN 68.07 1.20 71.64 1.10
BERT-DPCNN 69.01 1.10 70.38 1.00
ERNIE 85.71 0.61 87.08 0.51
ERNIE-CNN 87.36 0.49 90.76 0.46
The Result of the Classification
The Analysis of Efficiency in Question Processing
问题类别 q s i m均值 k r均值 k r '均值 k d i f均值 q c o n均值
事实类(29) 0.958 1.770 1.753 0.034 0.991
方法类(25) 0.920 1.657 1.467 0.600 1.020
原因类(13) 0.768 2.769 2.231 0.846 0.859
The Result of Answer Quality Evaluation Average
The Example of a Double Layer Policy Search Query
The Knowledge Retrieval of Related Supporting Policies
The Analysis of Efficiency in Knowledge Retrieving
政府部门 天津市人才奖补政策内容汇总
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天津市滨海高新区管委会 《天津滨海高新区促进信息技术应用创新产业发展办法》第5条规定:年收入达30万元的人才…
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配套政策 天津市人才奖补相关配套政策内容汇总
保险政策 天津市医疗保障局《天津市基本医疗保险付费总额管理办法(试行)》…
子女政策 天津市人社局《天津市就业困难人员认定办法》…
The Example of Policy Associated Answer Generation (Part)
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