Computer Science ›› 2018, Vol. 45 ›› Issue (6): 183-186.doi: 10.11896/j.issn.1002-137X.2018.06.032

• Artificial Intelligence • Previous Articles     Next Articles

Rough Set Based Knowledge Predicate Analysis of Chinese Knowledge Based Question Answering

HAN Zhao1,3, MIAO Duo-qian1,2, REN Fu-ji2,3   

  1. College of Electronics and Information Engineering,Tongji University,Shanghai 201804,China1;
    Key Laboratory of Embedded System and Service Computing,Ministry of Education,Tongji University,Shanghai 201804,China2;
    Faculty of Engineering,Tokushima University,Tokushima 7708506,Japan3
  • Received:2017-12-18 Online:2018-06-15 Published:2018-07-24

Abstract: In knowledge based question answering system,the performance of knowledge predicate analysis can affect the overall match result of knowledge triple.The knowledge predicate analysis of Chinese short question is difficult because of the uncertainty of Chinese knowledge predicate representation.Based on the rough set theory,a new definition of knowledge predicate analysis of knowledge based question snswering was given,and a new method was proposed to analyze the knowledge predicate of question.It can reduce the words which are weakly related with the knowledge predi-cate,and then the words which are more related with knowledge predicate representation will be used to match the knowledge triples to improve the overall performance of system.The experiment results verify the validity of the method.

Key words: Information retrieval, Knowledge based question answering, Question answering system, Rough set, Short text similarity

CLC Number: 

  • TP391
[1]MILLER E.An Introduction to the Resource Description Frame-work[J].Journal of Library Administration,2001,34(3/4):245-255.
[2]DU Z Y,YANG Y,HE L,et al.Question answering system of electric business field based on chinese knowledge map[J].Computer Applications and Software,2017,34(5):153-159.(in Chinese)
[3]ZHANG K L,LI W G,WANG H L,et al.Ontology-based Question Answering System for Aviation Domain[J].Journal of Chinese Information Processing,2015,29(4):192-198.(in Chinese)
[4]XIE Z,ZENG Z,ZHOUG,et al.Topic enhanced deep structured semantic models for knowledge base question answering[J].Scien-ce China(Information & Sciences),2017,60(11):110103.
[5]ZHAN C D,LING Z H,DAI L R.Learning Word Embeddings for Paraphrase Scoring in Knowledge Base Based Question An-swering[J].Pattern Recognition and Artificial Intelligence,2016,29(9):825-831.(in Chinese)
[6]ZENG S,WANG S,YUAN Y,et al.Towards Knowledge Automation:A Survey on Question Answering Systems[J].Acta Automatica Sinica,2017,43(9):1491-1508.(in Chinese)
[7]LIU K,ZHANG Y Z,JI G L,et al.Representation Learning for Question Answering over Knowledge Base:An Overview[J].Acta Automatica Sinica,2016,42(6):807-818.(in Chinese)
[8]WANG Y,REN F J,QUAN C Q.Review of Dialogue Management Methods in Spoken Dialogue System[J].Computer Scien-ce,2015,42(6):1-7,27.(in Chinese)
[9]ZHANG Z Z,MIAO D Q,YUE X D.Similarity measure for short texts using topic models and rough sets[J].Journal of Computational Information Systems,2013,9(16):6603-6611.
[10]YI G X,HU H P.A Web Search Result Clustering Based on Tolerance Rough Set[J].Journal of Computer Research and Development,2006,43(2):275-280.(in Chinese)
[11]LIU H,LIU D Y,PEI Z L,et al.A Feature Weighting Scheme for Text Categorization Based on Feature Importance[J].Journal of Computer Research and Development,2009,46(10):1693-1703.(in Chinese)
[12]THANH N C,YAMADA K.Document Representation and Clustering with WordNet Based Similarity Rough Set Model[J].International Journal of Computer Science Issues,2011,8(5):1-8.
[13]FAN T F,LIAU C J.Rough set-based concept mining from social networks[C]//IEEE International Conference on Fuzzy Systems.IEEE,2016:663-670.
[14]CAO L,HUANG G,CHAI W.A knowledge discovery model for third-party payment networks based on rough set theory[J].Journal of Intelligent & Fuzzy Systems,2017,33(1):1-9.
[15]DAI R,DUAN X.Research on Knowledge Acquisition of Motorcycle Intelligent Design System Based on Rough Set[M]//Computer and Computing Technologies in Agriculture V.Springer Berlin Heidelberg,2012:16-27.
[16]CHEN X G,DUAN S,WANG L D.Research on trend prediction and evaluation of network public opinion[J].Concurrency &Computation Practice & Experience,2017,29(4):e4212.
[18]PAWLAK Z.Rough set approach to knowledge-based decision support[J].European Journal of Operational Research,1997,99(1):48-57.
[19]HUANG X,WEI B,ZHANG Y.Automatic Question-Answering Based on Wikipedia Data Extraction[C]//International Conference on Intelligent Systems and Knowledge Engineering.IEEE,2016:314-317.
[21]DUAN N.Overview of the NLPCC-ICCPOL 2016 Shared Task:Open Domain Chinese Question Answering[C]//International Conference on Computer Processing of Oriental Languages.Springer International Publishing.2016:942-948.
[22]PEDREGOSA F,GRAMFORT A,MICHEL V,et al.Scikitlearn:Machine Learning in Python[J].Journal of Machine Learning Research,2011,12(10):2825-2830.
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