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
[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.
(Ministry of Industry and Information Technology. Notice of the Small and Medium-Sized Enterprise Bureau of the Ministry of Industry and Information Technology on the Issuance of the Recommended Catalogue of Digital Empowerment Service Products and Activities for Small and Medium-Sized Enterprises(Phase I)[EB/OL].(2020-04-21). https://www.miit.gov.cn/jgsj/qyj/wjfb/art/2020/art_4d845224c9ee4d4aa061841fb3f6014b.html.)
(General Office of the State Council. Notice of the General Office of the State Council on the Issuance of Key Points of Government Affairs Publicity in 2021[EB/OL].(2021-04-23). http://www.gov.cn/zhengce/content/2021-04/23/content_5601602.htm.)
[3]
Graesser A C, Murachver T. Symbolic Procedures of Question Answering[A]//The Psychology of Questions[M]. London: Routledge, 2017: 15-88.
[4]
Carter M. Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence[M]. Edinburgh,UK: Edinburgh University Press, 2007.
[5]
Turing A M. Computing Machinery and Intelligence[A]//Parsing the Turing Test[M]. Dordrecht: Springer Netherlands, 2007: 23-65.
(Ye Haosheng. The Dilemma of Dualism and the Rising of Embodied Cognition Programme[J]. Journal of Psychological Science, 2011, 34(4): 999-1005.)
[7]
Kiefer F. Morphology and Pragmatics[A]//The Handbook of Morphology[M]. Oxford, UK: Blackwell Publishing Ltd., 2017: 272-279.
[8]
Bhati R, Prasad S S. Open Domain Question Answering System Using Cognitive Computing[C]// Proceedings of 2016 6th International Conference-Cloud System and Big Data Engineering(Confluence). 2016: 34-39.
(Yu Jing. Detection of Hotspot in Scientific Fields Based on Emerging Pattern Analysis of Social Q&A Community Contents[J]. Journal of the China Society for Scientific and Technical Information, 2021, 40(2): 213-222.)
[10]
Indurkhya N, Damerau F J. Handbook of Natural Language Processing[M]. Chapman and Hall/CRC, 2010.
[11]
Roberts K, Alam T, Bedrick S, et al. TREC-COVID: Rationale and Structure of an Information Retrieval Shared Task for COVID-19[J]. Journal of the American Medical Informatics Association, 2020, 27(9): 1431-1436.
doi: 10.1093/jamia/ocaa091
pmid: 32365190
(Wen Youkui, Wen Hao, Qiao Xiaodong. Research on the Methods of Information Science and Artificial Intelligence Fusion Innovation[J]. Journal of the China Society for Scientific and Technical Information, 2019, 38(7): 722-730.)
[13]
Soares M A C, Parreiras F S. A Literature Review on Question Answering Techniques, Paradigms, and Systems[J]. Journal of King Saud University-Computer and Information Sciences, 2020, 32(6): 635-646.
doi: 10.1016/j.jksuci.2018.08.005
[14]
Abacha A B, Zweigenbaum P. MEANS: A Medical Question-Answering System Combining NLP Techniques and Semantic Web Technologies[J]. Information Processing & Management, 2015, 51(5): 570-594.
doi: 10.1016/j.ipm.2015.04.006
[15]
Abdi A, Idris N, Ahmad Z. QAPD: An Ontology-Based Question Answering System in the Physics Domain[J]. Soft Computing, 2018, 22(1): 213-230.
doi: 10.1007/s00500-016-2328-2
[16]
Agarwal A, Sachdeva N, Yadav R K, et al. EDUQA: Educational Domain Question Answering System Using Conceptual Network Mapping[C]// Proceedings of 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing. 2019: 8137-8141.
[17]
Kourtin I, Mbarki S, Mouloudi A. A Legal Question Answering Ontology-Based System[C]// Proceedings of International Conference on Automatic Processing of Natural-Language Electronic Texts with NooJ. 2020: 218-229.
(Chen Jinghao, Zeng Zhen, Li Gang. A Question Answering System for “the Belt and Road” Investment Based on Knowledge Graph[J]. Library and Information Service, 2020, 64(12): 95-105.)
doi: 10.13266/j.issn.0252-3116.2020.12.011
[19]
谭云丹. 科技政策智能问答系统架构及关键算法研究[D]. 重庆: 重庆邮电大学, 2020.
[19]
(Tan Yundan. Research on Architecture and Key Algorithm of Question Answering for Science and Technology Policies[D]. Chongqing: Chongqing University of Posts and Telecommunications, 2020.)
(Huo Chaoguang, Qian Yi, Qi Tianjiao. The Construction and Analysis of Epidemic Prevention Policy Knowledge Graph Based on Open Administrative Documents[J]. Archives Science Bulletin, 2021(2): 53-62.)
(Wu Kaibiao, Lang Yuxiang, Dong Yu. Mining Policy Text Relevance with Syntactic Structure and Semantic Information[J]. Data Analysis and Knowledge Discovery, 2022, 6(5): 20-33.)
[23]
Kryftis Y, Grammatikou M, Kalogeras D, et al. Policy-Based Management for Federation of Virtualized Infrastructures[J]. Journal of Network and Systems Management, 2017, 25(2): 229-252.
doi: 10.1007/s10922-016-9390-z
(Li Hanji, Chen Haiqing. Philosophical Reflection on the Technical Dilemma of Machine Translation[J]. Journal of Dalian University of Technology(Social Sciences), 2020, 41(6): 122-128.)
[25]
Kreutzer R T, Sirrenberg M. Understanding Artificial Intelligence[M]. Cham: Springer International Publishing, 2020.
(Li Chao, Chai Yumei, Nan Xiaofei, et al. Research on Problem Classification Method Based on Deep Learning[J]. Computer Science, 2016, 43(12): 115-119.)
doi: 10.11896/j.issn.1002-137X.2016.12.020
[27]
Tomasello M. Cognitive Linguistics[A]//A Companion to Cognitive Science[M]. Oxford, UK: Blackwell Publishing Ltd., 2017: 477-487.
(Li Jinpeng, Zhang Chuang, Chen Xiaojun, et al. Survey on Automatic Text Summarization[J]. Journal of Computer Research and Development, 2021, 58(1): 1-21.)
[29]
Ganesan K. ROUGE 2.0: Updated and Improved Measures for Evaluation of Summarization Tasks[OL]. arXiv Preprint, arXiv:1803.01937.
(Hua Bin, Wu Nuo, He Xin. Integrating Expert Reviews for Government Information Projects with Knowledge Fusion[J]. Data Analysis and Knowledge Discovery, 2021, 5(10): 124-136.)
(Wang Chuncheng. To Achieve Precision of Policy and Policy with Precision: A Significant Orientation of Public Policy in Precisiondepended Times[J]. Chinese Public Administration, 2018(1): 51-57.)
[32]
Sun Y, Wang S H, Li Y K, et al. ERNIE 2.0: A Continual Pre-training Framework for Language Understanding[OL]. arXiv Preprint, arXiv: 1907.12412.
(Ma Xiangdong, Zhang Wenkui, Liu Dingyi. The Changing Course and Orientation of Local Government’s Investment Promotion and Capital Introduction Policies: 1978—2021[J]. Reform, 2021(8): 131-144.)
(General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China, Standardization Administration of the People’s Republic of China. Format Specification for Electronic Official Document of Party and Government Organs—Part 1: Official Document Structure: GB/T 33476.1—2016[S]. Beijing: Standards Press of China, 2016.)