Computer Science ›› 2020, Vol. 47 ›› Issue (6): 210-218.doi: 10.11896/jsjkx.190700194

• Artificial Intelligence • Previous Articles     Next Articles

Knowledge-driven Method Towards Dynamic Partners Recommendation in Inter-enterprise Collaboration

WANG Tie-xin1,2, LI Wen-xin1, CAO Jing-wen1, YANG Zhi-bin1,2, HUANG Zhi-qiu1,2, WANG Fei1   

  1. 1 College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
    2 Key Laboratory of Safety-Critical Software (Nanjing University of Aeronautics and Atronautics),Ministry of Industry and Information Technology,Nanjing 210016,China
  • Received:2019-07-27 Online:2020-06-15 Published:2020-06-10
  • About author:WANG Tie-xin,born in 1987,Ph.D,assistant professor,M.S supervisor.His main research interests include model-driven engineering and collaboration management,etc.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61872182) and Fundamental Research Funds for the Central Universities (NJ2018014).

Abstract: The rapid development of information technology strongly promotes the process of market globalization.The trend of economic globalization has brought unprecedented opportunities and challenges to small and medium-sized enterprises (SMEs).Enterprises can no longer survive in an isolated-island way.In order to quickly respond to the changing market demand,SMEs need to establish dynamic collaborative relationship with other enterprises while focusing on their core business.To solve the problem of how to construct dynamic collaborative enterprise alliance efficiently,a method of dynamically recommending the best partners in the process of inter-enterprise collaboration is proposed by building domain ontologies and using semantic detection technology.This method aims to break through the defects of traditional enterprise collaboration,such as “fixed cooperative participants” and “single cooperative mode”,and can quickly and efficiently recommend competent participants considering the matching between the specific cooperative goals (& preferences) and capabilities and attributes.By studying enterprise modeling and enterprise collaboration managementand summarizing the research status of model-driven enterprise collaboration construction methods,a meta-model to describe the context of inter-enterprise collaboration is defined.Furthermore,the corresponding domain ontologies and semantics detection methods are proposed to improve the efficiency of dynamic recommendation of partners.Finally,the effectiveness of this recommendation method is demonstrated by a case study of disassembly and connection machine manufacturing and its performance is evaluated.

Key words: Inter-enterprise collaboration, Domain ontology, Knowledge-driven, Semantic checking, Meta-model

CLC Number: 

  • TP311
[1]CAMARINHA-MATOS L.Classes of Collaborative Networks [M]//Encyclopedia of Networked and Virtual Organizations.Information Science Reference,2008:193-198.
[2]MONTARNAL A,MU W X,BENABEN F,et al.Automated deduction of cross-organizational collaborative business processes[J].Information Sciences,2018,453:30-49.
[3]TOUZI J,LORRE J P,BENABEN F,et al.Interoperability through Model-based Generation:The Case of the Collaborative Information System (CIS) [M]//Enterprise Interoperability.London:Springer,2007.
[4]LI L.Effects of enterprise technology on supply chain collaboration:analysis of China-linked supply chain [J].Enterprise Information Systems,2012,6(1):55-77.
[5]KONSTANTAS D,BOURRIERES J P,LEONARD M,et al.Interoperability of enterprise software and applications [M].Springer Science & Business Media,2006,1.
[6]IDE N,PUSTEJOVSKY J.What does interoperability mean,anyway? Toward an operational definition of interoperability for language technology [C]//Proceedings of the Second International Conference on Global Interoperability for Language Resources.Hong Kong,China,2010.
[7]CRISPIM JA,SOUSA J P.Partner selection in virtual enterprises[J].International Journal of Production Research,2010,48(3):683-707.
[8]SADIGH B L,NIKGHADAM S,OZBAYOGLU A M,et al.An ontology-based multi-agent virtual enterprise system (omave):part 2:partner selection[J].International Journal of Computer Integrated Manufacturing,2017,30(10):1072-1092.
[9]SHA D,CHE Z.Supply chain network design:partner selection and production/distribution planning using a systematic model[J].Journal of the Operational Research Society,2006,57(1):52-62.
[10]BENABEN F,BOISSEL-DALLIER N,PINGAUD H,et al.Semantic issues in model-driven management of information system interoperability[J].International Journal of Computer Integrated Manufacturing,2013,26(11):12.
[11]GARCIA-CRESPO A,RUIZ-MEZCUA B,LOPEZ-CUADRADO J,et al.Semantic model for knowledge representation in e-business[J].Knowledge-Based Systems,2011,24(2):282-296.
[12]CHEN D,DOUMEINGTSB G,VERNADATC F.Architectures for enterprise integration and interoperability:Past,present and future [J].Computers in Industry,2009,59(7):647-659.
[13]KOSANKE K,VERNADAT F,ZELM M.CIMOSA:enterprise engineering and integration [J].Computers in Industry,1999,40(2/3):83-97.
[14]BENABEN F,MU W X,BOISSEL-DALLIER N,et al.Supporting interoperability of collaborative networks through enginee-ring of a service-based Mediation Information System (MISE 2.0) [J].Enterprise Information Systems,2014,9(5/6):1-27.
[15]ALI N H,IBRAHIM N S.Porter stemming algorithm for semantic checking [C]//Proceedings of 16th International Conference on Computer and Information Technology.2012:253-258.
[16]FENG Y,BAGHERI E,ENSAN F,et al.The state of the art in semantic relatedness:a framework for comparison[J].The Knowledge Engineering Review,2017,32:1-30.
[17]KAPPEL G,KARGL H,KRAMLER G,et al.Matching Metamodels with Semantic Systems-An Experience Report [M]//BTW Workshops on Model Management.2007:38-52.
[18]SHVAIKO P,EUZENAT J.A survey of schema-based matc-hing approaches [M]//Journal on Data Semantics IV.Springer Berlin Heidelberg,2005:146-171.
[19]LIN F,SANDKUHL K.A Survey of Exploiting WordNet inOntology Matching [C]//Artificial Intelligence in Theory & Practice Ii.Italy:DBLP,2008.
[20]LI P F,ZHOU G D,ZHU Q M.Semantics-Based Joint Model of Chinese Event Trigger Extraction[J].Journal of Software,2016,27(2):280-294.
[21]RAO Y,WU L W,WANG Y M,et al.Research Progress on Emotional Computation Technology Based on Semantic Analysis[J].Journal of Software,2018(8):2397-2426.
[22]THOMAS K.Matters of (Meta-) Modeling [J].Software and Systems Modeling,2006,5(4):369-385.
[23]FAVRE J M,NGUYEN T.Towards a megamodel to model software evolution through transformations[J].Electronic Notes in Theoretical Computer Science,2005,127(3):59-74.
[24]ALBERTO S R D.Model-driven engineering:A survey supported by the unified conceptual model[J].Computer Languages,Systems & Structures,2015,43:S1477842415000408.
[25]JOUAULT F,BÉZIVIN J.KM3:A DSL for Metamodel Specification[C]//Ifip Wg 61 International Conference on Formal Methods for Open Object-based Distributed Systems.2006.
[26]OMG.Object Management Group-MDA (Model Driven Architecture) Guide Version 1.0.1[OL].
[27]OMG.Object Management Group-Meta Object Facility (MOF) Core Specification,v2.4.2[OL].
[28]WANG T X,MONTARNAL A,TRUPTIL S,et al.A Semantic-checking based Model-driven Approach to Serve Multi-organization Collaboration[OL].
[29]MALONE T W,CROWSTON K,HERMAN G.A.Organizing business knowledge:the mit process handbook [J].Journal of Product Innovation Management,2010,22(2):218-219.
[30]United Nations and Statistical Division.International Standard industrial classification of all economic activities (ISIC) [M].New York:United Nations,2008.
[31]PORTER M F.An algorithm for suffix stripping[J].Program,1980,14(3):130-137.
[32]COHEN W,RAVIKUMAR P,FIENBERG S.A comparison of string metrics for matching names and records[C]//Kdd Workshop on Datacleaning and Object Consolidation.2003:73-78.
[33]HIRSCHBERG D.Serial computations of Levenshtein distances[OL].
[34]FELLBAUM C.WordNet.Blackwell Publishing Ltd[OL].
[35]WU F F,LI R M,HUANG L C,et al.Cross-industry R&D Partners Identification and Selection of Enterprises [OL].
[36]LI B Z,GAO S.Research on the mutualism partner selection of enterprise collaborative original innovation [J].J. Harbin Inst. Technol.,2019,40(7):1367-1374.
[1] YANG Li, MA Jia-jia, JIANG Hua-xi, MA Xiao-xiao, LIANG Geng, ZUO Chun. Requirements Modeling and Decision-making for Machine Learning Systems [J]. Computer Science, 2020, 47(12): 42-49.
[2] LIU Yao, SHUAI Yuan-hua, GONG Xing-wei and HUANG Yi. Study on Text Segmentation Based on Domain Ontology [J]. Computer Science, 2018, 45(1): 128-132.
[3] ZHOU Wen-bo, LIU Hong-jia, LIU Lei, ZHANG Peng and LV Shuai. Meta-modeling Approach of Message Interaction in Service [J]. Computer Science, 2017, 44(4): 24-29.
[4] QIAN Ye, LI Tong, YU Yong, SUN Ji-hong, YU Qian and PENG Lin. Approach to Modeling Software Evolution Process for Synchronous Interaction [J]. Computer Science, 2016, 43(8): 154-158.
[5] LI Yi-xiao, LI Hong-wei, SHEN Li-wei and ZHAO Wen-yun. Automatic Ontology Population Based on Heuristic Rules [J]. Computer Science, 2016, 43(3): 213-219.
[6] GUO Peng, LI Ya-hui, SUN Lei and CAI Xiao-le. UML Model to Simulink Model Transformation Method in Design of Embedded Software [J]. Computer Science, 2016, 43(2): 192-198.
[7] TANG Cheng-hua, WANG Li-na, QIANG Bao-hua, TANG Shen-sheng and ZHANG Xin. Static Security Policy Consistency Detection Based on Semantic Similarity [J]. Computer Science, 2015, 42(8): 166-169.
[8] LIU Huan-huan, MA Zhi-yi and CHEN Hong-jie. Meta-model of PaaS-based Cloud Application’s Deployment Environment [J]. Computer Science, 2015, 42(10): 45-49.
[9] ZHAO Wen-dong,TAO Xiao-zhen,PENG Lai-xian and TIAN Chang. Function Semantic-based Web Service Description and Pre-filter Method [J]. Computer Science, 2013, 40(11): 222-227.
[10] . Conceptual Modeling Method of Simulation System Based on [J]. Computer Science, 2012, 39(5): 137-140.
[11] ZHU Zheng-zhou. E-Learning Services Discovery Algorithm Based on Context Aware [J]. Computer Science, 2012, 39(2): 136-142.
[12] . On-demand Service Discovery Method of Networked Software [J]. Computer Science, 2012, 39(1): 96-100.
[13] YONG Xi,CAO Cun-gen,BAI Ai-ming. Research and Application of Ontology-based Intelligent Planning Method [J]. Computer Science, 2011, 38(2): 175-178.
[14] HUANG Long,YANG Yu-hang. U2TP Test Model Profiling for Web Services [J]. Computer Science, 2010, 37(9): 135-136.
[15] ZHANG Liang,QU Zhen-xin,DING Song,TANG Sheng-qun. Semantic Retrieval Based on Weighted Domain Ontology [J]. Computer Science, 2010, 37(7): 165-168.
Full text



[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .