Computer Science ›› 2020, Vol. 47 ›› Issue (1): 199-204.doi: 10.11896/jsjkx.181202351

• Artificial Intelligence • Previous Articles     Next Articles

Comprehensive Calculation of Semantic Similarity of Ontology Concept Based on SA-BP Algorithm

XU Fei-xiang1,YE Xia1,LI Lin-lin1,CAO Jun-bo1,WANG Xin2   

  1. (Academy of Combat Support,Rocket Force University of Engineering,Xi’an 710025,China)1;
    (Information Systems Department,University of Maryland,Baltimore,Maryland 21250,USA)2
  • Received:2018-12-18 Published:2020-01-19
  • About author:XU Fei-xiang,born in 1995,postgra-duate.His main research interests include ontology integration and semantic network;YE Xia,born in 1977,Ph.D,associate professor.Her main research interests include database technology and computer network.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61702525).

Abstract: There are heterogeneous problems in indicators that are established by different combat forces when evaluating and testing command information systems,which leads to great difficulties in information interaction and data sharing.In order to achieve mapping and integration of indicator’s ontology-concept,building a unified global indicator ontology tree is an effective solution.In this case,the accuracy of similarity calculation for ontology-concept becomes crucial.Aiming at the problem of low accuracy in the existing ontology-concept similarity calculation model,a comprehensive simi-larity calculation model based on BP neural network algorithm which is improved by Simulated Annealing (SA-BP),was proposed.This paper first improved the classical similarity calculation models based on semantic distance,information content and conceptual attribute.Besides,a similarity calculation model in view of concept’s sub-node coincidence was proposed in order to avoid the subjectivity of artificially determined weights and the inaccuracy of simple linear weighting in existing models.At last,a training test on the comprehensive similarity calculation model was performed,while the sample data were extracted from ontology-concept of variable evaluation indicators that come from command information systems established by different departments of combat forces.Experimental data show that compared with PSO-BP calculation model and principal-component linear weighted calculation model,the comprehensive similarity calculation model based on SA-BP algorithm achieves strong correlation,since its results and its Pearson correlation coefficient of the results evaluated by experts are increased by 0.0695 and 0.1351 respectively.The experimental results verify that,after training,SA-BP algorithm can converge better and achieve higher accurate when calculating ontology-concept similarity.Hence,key issues of integration for ontology-concept can be effectively solved.

Key words: BP neural network, Ontology integration, Semantic similarity calculation, Simulated annealing algorithm, Sub-nodecoincidence

CLC Number: 

  • TP391
[1]SCHADD F C,ROOS N.Word-Sense Disambiguation for Onto- logy Mapping:Concept Disambiguation using Virtual Documents and Information Retrieval Techniques[J].Journal on Data Semantics,2015,4(3):167-186.
[2]GAO W,FARAHANI M R,ASLAM A,et al.Distance learning techniques for ontology similarity measuring and ontology mapping[J].Cluster Computing,2017,20(2):959-968.
[3]RADA R,MILI H,BICKNELL E,et al.Development and application of a metric on semantic nets[J].IEEE Transactions on Systems,Man,and Cybernetics,2002,19(1):17-30.
[4]WU Z,PALMER M.Verb Semantics and Lexical Selection
[C]∥Proceedings of 32nd Annual Meeting on Association for Computational Linguistics.LasCruces,New Mexico,1994:133-138.
[5]LEACOCK C,CHODOROW M.Combining Local Context and WordNet Similarity for Word Sense Identification[M].WordNet:An Electronic Lexical Database,1998.
[6]GOBLE A,STEVENS J R,BRASS C A,et al.Investigating semantic similarity measures across the Gene Ontology:the relationship between sequence and annotation[M].Oil Bhales of the World:Pergamon Press,2003.
[7]RESNIK P.Semantic Similarity in a Taxonomy:An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language[J].Journal of Artificial Intelligence Research,2011,11(1):95-130.
[8]LIN D.An Information-Theoretic Definition of Similarity[C]∥International Conference on Machine Learning.New Brunswick,NJ,1998.
[9]TVERSKY A.Features of Similarity[J].Readings in Cognitive Science,1988,84(4):290-302.
[10]WAN S,ANGRYK R A.Measuring semantic similarity using WordNet-based Context Vectors[C]∥2007 IEEE International Conference on Systems,Man and Cybernetics.Montreal,2007:908-913.
[11]ZHANG Z P,TIAN S X,LIU H Q.A Comprehensive Method for Calculating Ontology Similarity[J].Computer Science,2008,35(12):142-145.
[12]LI Y,ZA B.An approach for measuring semantic similarity between words using multiple information sources[J].IEEE Transactions on Knowledge & Data Engineering,2003,15(4):871-882.
[13]ZHANG H Y,WEN C Y,LIU D B,et al.Improved ontology-based semantic similarity calculation[J].Computer Engineering and Design,2015,36(8):2206-2210.
[14]ZHENG Z Y,RUAN C Y,LI L,et al.Research on Adaptive Synthetic Weighting Algorithm for Ontology Semantic Similarity[J].Computer Science,2016,43(10):242-247.
[15]GAO X R,XU Y Z.Research on Improved Model for Concept Similarity Computation in Domain Ontology and Application[C]∥2017 International Conference on Robots & Intelligent System (ICRIS).Huai’an,2017:257-261.
[16]HAN X R,WANG Q S,GUO Y,et al.Semantic similarity measure of geographic ontology based on PSO-BP algorithm[J].Computer Engineering and Applications,2017,53(8):32-37.
[17]GUO X H,PENG Q,DENG H,et al.WordNet word similarity calculation based on edge weight [J].Computer Engineering and Application,2018,54(1):172-178.
[18]FAN M,ZHANG Y,LI J.Word similarity computation based on HowNet[C]∥International Conference on Fuzzy Systems & Knowledge Discovery.IEEE,2016.
[19]LI Y,GAO D Q.Research on Entity Similarity Computation in Knowledge Map [J].Chinese Journal of Information Science,2017,31(1):145-151,159.
[20]WANG S,NA Z,LEI W,et al.Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method[J].Renewable Energy,2016,94(1):629-636.
[21]METROPOLIS N,ROSENBLUTH A W,ROSENBLUTH M N,et al.Equation of State Calculations by Fast Computing Machines[J].The Journal of Chemical Physics,2004,1087(1953):21.
[22]KIRKPATRICK S,VECCHI M P.Optimization by simulated annealing[M].Spin Glass Theory and Beyond:An Introduction to the Replica Method and Its Applications,1987.
[23]MAMANO N,HAYES W B.SANA:Simulated Annealing far outperforms many other search algorithms for biological network alignment[J].Bioinformatics,2017,33(14):1-9.
[24]ZHOU A W,ZHAI Z H,LIU H T.An improved BP neural network algorithm based on simulated annealing algorithm [J].Microelectronics and Computer,2016,33(4):144-147.
[25]DE WINTER J C,GOSLING S D,POTTER J.Comparing the Pearson and Spearman Correlation Coefficients Across Distributions and Sample Sizes:A Tutorial Using Simulations and Empirical Data[J].Psychological Methods,2016,21(3):273.
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