Computer Science ›› 2020, Vol. 47 ›› Issue (12): 131-138.doi: 10.11896/jsjkx.191000161

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Extraction of Water Conservancy Spatial Relationship Words Based on Bootstrapping

XIANG Ying, FENG Jun, XIA Pei-pei, LU Jia-min   

  1. College of Computer and Information Hohai University Nanjing 211100,China
  • Received:2019-10-24 Revised:2020-03-20 Online:2020-12-15 Published:2020-12-17
  • About author:XIANG Ying,born in 1995postgra-duateis a member of China Computer Federation.Her main research interests include relation extraction and so on.
    FENG Jun,born in 1969Ph.DprofessorPh.D supervisoris a member of China Computer Federation.Her main research interests include spatiotemporal data managementintelligent data processingdata mining and water conservancy informatization.
  • Supported by:
    National Key R&D Program of China(2018YFC0407901),Young Scientists Fund of the National Natural Science Foundation of China(61602151) and Jiangsu Collaborative Innovation Center for Cultural Creativity(XYN1702).

Abstract: At presentthe following problems are found in the extraction of water conservancy spatial relational words in the process of using water conservancy domain database to construct knowledge map.Firstthere are few water conservancy object spatial relational words in the databasewhich is difficult to meet the needs of query.Secondthe relationship between water conservancy objects is complex and it is too laborious to rely on manual construction.In order to solve the above problemsfirstlythis paper extracts spatial relation words from professional high-quality water conservancy official documents to form seed sets.Thenit expands spatial relationship words through external dictionariesand combines corpus to extract water-related spatial relationship words Syntactic pattern.Finallythrough the generalized syntactic patternspatial relation words are extracted from large-scale water conservancy text dataspatial relationship triples are generatedand then used as seed sets.Repeating the above steps can gradually expand and construct water resources.This method can obtain a large number of spatial semantic syntactic patterns and spatial relationship tuples from the corpus with a small amount of manual operationsgradually expand the construction and eventually form a dictionary of water conservancy spatial relationship words.The word dictionary plays an important role in expanding the knowledge map of water conservancy objects and improving the accuracy of intelligent retrieval.

Key words: Knowledge graph, Relationship extraction, Spatial relationship, Water conservancy field

CLC Number: 

  • TP391.1
[1] CHENG J G,FENG J,YANG P,et al.Research on key techno-logies of water resources data directory service[J].Water Resources Informationization,2014(6):18-21.
[2] FENG J,TANG Z X,ZHU Y L,et al.Study on metadata definition of water resources information catalog service[J].Water Resources Informationization,2011(S1):19-22.
[3] ZHAO J,LIU K,ZHOU G Y,et al.Open Text Information Extraction[J].Journal of Chinese Information Processing,2011,25(6):98-110.
[4] LIU Y.Construction of Jilin Regional Knowledge Map Based on Geographic Ontology[D].Beijing:Beijing Jiaotong University,2017.
[5] HU C X,FU Y Q,ZHONG M Y.Extension of Semantic Query Based on Domain Ontology[J].Journal of Computer Systems,2012,21(7):83-89.
[6] JURAFSKY D,MARTINJ H.Speech and Language Processing[OL].http://web.stanford.edu/~jurafsky/slp3/.
[7] SCHUTZ A,BUITELAAR P.RelExt:a tool for relation extraction from text in ontology extension[C]//International Confe-rence on the Semantic Web.2005.
[8] RINK B,HARABAGIU S.Utd:Classifying semantic relations by combining lexical and semantic resources[C]//Proceedings of the 5th International Workshop on Semantic Evaluation.2010:256-259.
[9] DODDINGTON G R,MITCHELL A,PRZYBOCKI M A,et al.The Automatic Content Extraction (ACE) Program Tasks,Data,and Evaluation[C]//Language Resources and Evaluation.2004.
[10] HUANG X,YOU H L,YU Y.A Summary of Research on Relationship Extraction Technology [J].Modern Library and Information Technology ,2013,29(11):3039.
[11] XU F Y,USZKOREIT H,KRAUSE S,et al.Boosting Relation Extraction with Limited ClosedWorld Knowledge[C]//23rd International Conference on Computational Linguistics(COLING 2010).Beijing:Association for Computational Linguistics,2010.
[12] LI R J,ZHANG J,ZHANG X M,et al.Web information extraction in health field[J].Journal of Computer Applications,2016,36(1):163-170.
[13] ABNEYSP.Bootstrapping[C]//Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics.Stroudsburg:Association for Computational Linguistics,2002:360-367.
[14] YU L,LU F,LIU X L.Bootstrapping method for extractingopen geographic entity relations[J].Journal of Surveying and Mapping,2016,45(5):616-622.
[15] DENG M,XU R,LI Z L,et al.Research on the Transformation Method of Natural Language Spatial Relations and Metric Spatial Relations in Spatial Queries:Taking Area Targets as Examples[J].Journal of Surveying and Mapping,2009,38(6):527-531.
[16] MEI J J,ZHU Y M,GAO Y Q.Synonym Ci Lin (Second Edition)[M].Shanghai:Shanghai Dictionary Publishing House,1996.
[17] LI H G.Research on Chinese named entity relationship extraction based on location and semantic features[D].Hefei:Hefei University of Technology ,2011.
[18] CHEN C.Research on Internet-based binary entity relation extraction[D].Shanghai:East China Normal University,2013.
[19] LU S,BAI S.Quantitative description of the effective range of word context in natural language processing[J].Chinese Journal of Computers,2001,24(7):742-747.
[20] BUNKYOKU H,MATSUO Y,ISHIZUKA M.Relation Extraction from Wikipedia Using Subtree Mining Dat P.T.Nguyen[C]//National Conference on Artificial Intelligence.2013.
[21] SURDEANU M,TIBSHIRANI J,NALLAPATI R,et al.Multi-instance Multi-label Learning for Relation Extraction[C]//Joint Conference on Empirical Methods in Natural Language Processing &Computational Natural Language Learning.2012.
[22] CHE W,LI Z,LIU T.LTP:A Chinese Language Technology Platform[C]//23rd International Conference on Computational Linguistics,Demonstrations(COLING 2010).Beijing,China,2010.
[23] KLEIN D.Accurate Unlexicalized Parsing[C]//Proceedings of the 41st Meeting of the Association for Computational Linguistics.Sapporo,Japan,2003.
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