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Semantic web service composition using semantic similarity measures and formal concept analysis

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Abstract

One of the main assets of the Service Oriented Architecture (SOA) is composition, which consists in developing higher-level services by re-using well-known functionality provided by other services in a low-cost and rapid development process. In this paper, we present IDECSE a new integrated approach for composite services engineering. By considering semantic Web services, IDECSE addresses the challenge of fully automating the classification, discovery and composition while reducing development time and cost. The classification and the discovery processes rely on adequate semantic similarity measures. Both semantic and syntactic descriptions are integrated through specific techniques for computing similarity measures between services. Formal Concept Analysis (FCA) is used then to classify Web services into concept lattices in order to facilitate relevant services identification. A graph based semantic Web service composition process was proposed within the IDECSE framework. Using semantic similarities in grouping classes of services and in composing services shows a significant improvement compared to other approaches.

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Notes

  1. http://conexp.sourceforge.net/

  2. https://github.com/cog-isa/causal-app

  3. http://www.daml.org/

  4. http://projects.semwebcentral.org/projects/sws-tc

  5. https://jena.apache.org

  6. http://docs.oracle.com/javase/tutorial/jaxp/

References

  1. Abid A, Messai N, Rouached M, Abid M, Devogele T (2017) Semantic similarity based web services composition framework. In: Proceedings of the symposium on applied computing. ACM, pp 1319–1325

  2. Abid A, Messai N, Rouached M, Devogele T, Abid M (2014) Idecse: a semantic integrated development environment for composite services engineering. In: CAiSE (Forum/Doctoral Consortium), pp 105–112

  3. Abid A, Messai N, Rouached M, Devogele T, Abid M (2015) A semantic similarity measure for conceptual web services classification. In: 2015 IEEE 24th international conference on enabling technologies: infrastructure for collaborative enterprises (WETICE). IEEE, pp 128–133

  4. Acampora G, Gaeta M, Loia V, Vasilakos AV (2010) Interoperable and adaptive fuzzy services for ambient intelligence applications. ACM Trans Autonom Adaptive Sys (TAAS) 5(2):8

    Google Scholar 

  5. Al-Masri E, Mahmoud QH (2008) Investigating web services on the World Wide Web. In: Proceedings of the 17th international conference on World Wide Web. ACM, pp 795–804

  6. Azmeh Z, Driss M, Hamoui F, Huchard M, Moha N, Tibermacine C (2011) Selection of composable web services driven by user requirements. In: 2011 IEEE international conference on web services (ICWS). IEEE, pp 395–402

  7. Azmeh Z, Hamoui F, Huchard M, Messai N, Tibermacine C, Urtado C, Vauttier S (2011) Backing composite web services using formal concept analysis. In: Formal concept analysis. Springer

  8. Baryannis G, Danylevych O, Karastoyanova D, Kritikos K, Leitner P, Rosenberg F, Wetzstein B (2010) Service composition. In: Service research challenges and solutions for the future internet - S-Cube - towards engineering, managing and adapting service-based systems, pp 55–84

  9. Benouaret K, Benslimane D, Hadjali A (2011) On the use of fuzzy dominance for computing service skyline based on qos. In: 2011 IEEE international conference on web services (ICWS). IEEE, pp 540–547

  10. Chafle G, Das G, Dasgupta K, Kumar A, Mittal S, Mukherjea S, Srivastava B (2007) An integrated development environment for web service composition. In: ICWS. IEEE Computer Society, pp 839–847

  11. Chen F, Li M, Wu H, Xie L (2017) Web service discovery among large service pools utilising semantic similarity and clustering. Enterprise Information Systems 11 (3):452–469

    Article  Google Scholar 

  12. Chen F, Lu C, Wu H, Li M (2017) A semantic similarity measure integrating multiple conceptual relationships for web service discovery. Expert Syst Appl 67:19–31

    Article  Google Scholar 

  13. Cheniki N, Sam Y, Messai N, Belkhir A (2018) Context-aware and linked open data based service discovery. In: International conference on web engineering. Springer, pp 448–462

  14. Daagi M, Ouniy A, Kessentini M, Gammoudi MM, Bouktif S (2017) Web service interface decomposition using formal concept analysis. In: 2017 IEEE international conference on web services (ICWS). IEEE, pp 172–179

  15. Davey BA, Priestley HA (2002) Introduction to lattices and order. Cambridge University Press, Cambridge

  16. Elgazzar K, Hassan AE, Martin P (2010) Clustering WSDL documents to bootstrap the discovery of web services. In: 2010 IEEE international conference on web services (ICWS). IEEE, pp 147–154

  17. Elmaghraoui H, Zaoui I, Chiadmi D, Benhlima L (2011) Graph based e-government web service composition. arXiv:1111.6401

  18. Euzenat J, Shvaiko P, et al. (2007) Ontology matching, vol 18. Springer, Berlin

    MATH  Google Scholar 

  19. Fellah A, Malki M, Elci A (2019) A similarity measure across ontologies for web services discovery. In: Web services: concepts, methodologies, tools, and applications. IGI Global, pp 859–881

  20. Ganjisaffar Y, Abolhassani H, Neshati M, Jamali M (2006) A similarity measure for OWL-S annotated web services. In: Proceedings of the IEEE/WIC/ACM international conference on web intelligence. IEEE Computer Society, p 2006

  21. Ganter B, Wille R (2012) Formal concept analysis: mathematical foundations. Springer, Berlin

  22. Hasan MH, Jaafar J, Hassan MF (2014) Fuzzy-based clustering of web services’ quality of service: a review. J Commun 9(1):81–90

    Article  Google Scholar 

  23. Hatzi O, Vrakas D, Bassiliades N, Anagnostopoulos D, Vlahavas I (2013) The porsce ii framework: using ai planning for automated semantic web service composition. Knowl Eng Rev 28(02):137–156

    Article  Google Scholar 

  24. Kumara BT, Paik I, Chen W (2013) Web-service clustering with a hybrid of ontology learning and information-retrieval-based term similarity. In: 2013 IEEE 20th international conference on web services (ICWS). IEEE

  25. Kumara BT, Paik I, Chen W, Ryu KH (2014) Web service clustering using a hybrid term-similarity measure with ontology learning. Int J Web Services Res (IJWSR) 11(2):24–45

    Article  Google Scholar 

  26. Lécué F, Léger A (2006) A formal model for semantic web service composition. In: The semantic Web-ISWC 2006. Springer, pp 385–398

  27. Lécué F, Salibi S, Bron P, Moreau A (2008) Semantic and syntactic data flow in web service composition. In: 2008 IEEE international conference on web services. IEEE, pp 211–218

  28. Li W, Dai X, Jiang H (2010) Web services composition based on weighted planning graph. In: 2010 First international conference on networking and distributed computing. IEEE, pp 89–93

  29. Liu W, Wong W (2009) Web service clustering using text mining techniques. International Journal of Agent-Oriented Software Engineering

  30. Ma J-M, Zhang W-X, Cai S (2006) Variable threshold concept lattice and dependence space. In: International conference on fuzzy systems and knowledge discovery. Springer, pp 109–118

  31. Messai N (2009) Analyse de concepts formels guidée par des connaissances de domaine: application à la découverte de ressources génomiques sur le web. PhD thesis, Université Henri Poincaré-Nancy I

  32. Messai N, Devignes M-D, Napoli A, Smaïl-Tabbone M (2010) Using domain knowledge to guide lattice-based complex data exploration. In: ECAI, vol 215, pp 847–852

  33. Mohammed M, Amine CM, Fethallah H (2016) Leveraging fuzzy dominance relationship and machine learning for hybrid web service discovery. Int J Web Eng Technol 11(2):107–132

    Article  Google Scholar 

  34. Mouhoub ML, Grigori D, Manouvrier M (2014) A framework for searching semantic data and services with sparql. In: International conference on service-oriented computing. Springer, pp 123–138

  35. Naim H, Aznag M, Quafafou M, Durand N (2016) Probabilistic approach for diversifying web services discovery and composition. In: 2016 IEEE international conference on web services (ICWS). IEEE, pp 73–80

  36. Paolucci M, Kawamura T, Payne TR, Sycara K (2002) Semantic matching of web services capabilities. Springer, pp 333–347

  37. Papazoglou MP, Traverso P, Dustdar S, Leymann F (2007) Service-oriented computing: state of the art and research challenges. Computer 40(11):38–45

    Article  Google Scholar 

  38. Plebani P, Pernici B (2009) URBE: web service retrieval based on similarity evaluation. In: IEEE transactions on knowledge and data engineering

  39. Rahimi MR, Venkatasubramanian N, Mehrotra S, Vasilakos AV (2012) Mapcloud: mobile applications on an elastic and scalable 2-tier cloud architecture. In: 2012 IEEE fifth international conference on utility and cloud computing (UCC). IEEE, pp 83–90

  40. Rodriguez-Mier P, Pedrinaci C, Lama M, Mucientes M (2016) An integrated semantic web service discovery and composition framework. IEEE Trans Serv Comput 9(4):537–550

    Article  Google Scholar 

  41. Sánchez D, Batet M, Isern D, Valls A (2012) Ontology-based semantic similarity: a new feature-based approach. Expert Syst Appl 39(9):7718–7728

    Article  Google Scholar 

  42. Sangers J, Frasincar F, Hogenboom F, Chepegin V (2013) Semantic web service discovery using natural language processing techniques. Expert Syst Appl 40 (11):4660–4671

    Article  Google Scholar 

  43. Tarjan R (1972) Depth-first search and linear graph algorithms. SIAM J Comput 1(2):146–160

    Article  MathSciNet  Google Scholar 

  44. Tibermacine O, Tibermacine C, Cherif F (2013) Wssim: a tool for the measurement of web service interface similarity. In: Proceedings of the french-speaking conference on software architectures (CAL)

  45. Tversky A (1977) Features of similarity. Psychol Rev 84(4):327

    Article  Google Scholar 

  46. Van Der Merwe D, Obiedkov S, Kourie D (2004) Addintent: a new incremental algorithm for constructing concept lattices. In: International conference on formal concept analysis. Springer, pp 372–385

  47. Wu J, Chen L, Zheng Z, Lyu MR, Wu Z (2014) Clustering web services to facilitate service discovery. Knowl Inf Sys 38(1):207–229

    Article  Google Scholar 

  48. Yan Y, Xu B, Gu Z (2008) Automatic service composition using and/or graph. In: 2008 10th IEEE conference E-commerce technology, pp 335–338

  49. Zapater JJS, Escrivá DML, García FRS, Durá JJM (2015) Semantic web service discovery system for road traffic information services. Expert Syst Appl 42 (8):3833–3842

    Article  Google Scholar 

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Authors and Affiliations

  1. Easy Global Market, Sophia Antipolis, France

    Ahmed Abid

  2. University of Bahrain, Zallaq, Bahrain

    Mohsen Rouached

  3. University of Tours, Tours, France

    Nizar Messai

Authors
  1. Ahmed Abid
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  2. Mohsen Rouached
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  3. Nizar Messai
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Correspondence to Ahmed Abid.

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Abid, A., Rouached, M. & Messai, N. Semantic web service composition using semantic similarity measures and formal concept analysis. Multimed Tools Appl 79, 6569–6597 (2020). https://doi.org/10.1007/s11042-019-08441-z

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