SEMANTIC-BASED CLUSTERING OF WEB SERVICES

Authors

  • LEONARDO DE JESUS SILVA FORMAS/LASID/DCC/IM, Federal University of Bahia, Av. Adhemar de Barros, s/n, Ondina Salvador, Bahia 40170-110, Brazil
  • DANIELA BARREIRO CLARO FORMAS/LASID/DCC/IM, Federal University of Bahia, Av. Adhemar de Barros, s/n, Ondina Salvador, Bahia 40170-110, Brazil
  • DENIVALDO CICERO PAV~AO LOPES LESERC, Federal University of Maranh~ao Campus do Bacanga - CCET S~ao Luiz, Maranh~ao 40170-110, Brazil

Keywords:

Web Services, Clustering, Semantic-based Similarity

Abstract

The interoperability needed for the exchange of information between organizations is currently obtained through Service Oriented Architecture (SOA). Through the implementation of Web services, components can be described in a consistent and standardized way. However, there is a high number of published Web services, it is important to have some organization in order to determine specic areas of these services. Thus, our approach proposes creating semantically similar domains of Web services by applying clustering algorithms. Two clustering algorithms were adapted and evaluated. Each cluster was validated in order to analyze whether the Web services were grouped correctly. Our results evaluated each group and the set of categorized services.

 

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References

B. Adida, M. Birbeck, S. McCarron, and S. Pemberton. RDFa in XHTML: Syntax and processing,

October 2008.

E. Al-Masri and Q. H. Mahmoud. Investigating web services on the world wide web. In Proceedings

of the 17th International Conference on World Wide Web, WWW '08, pages 795{804, New York,

NY, USA, 2008. ACM.

K. Elgazzar, A. E. Hassan, and P. Martin. Clustering wsdl documents to bootstrap the discovery

of web services. In ICWS, pages 147{154. IEEE Computer Society, 2010.

T. Erl. Service-Oriented Architecture: Concepts, Technology, and Design. Prentice Hall PTR,

Upper Saddle River, NJ, USA, 2005.

B. S. Everitt, S. Landau, and M. Leese. Cluster Analysis. Wiley Publishing, 4th edition, 2009.

G. Gan, C. Ma, and J. Wu. Data clustering - theory, algorithms, and applications. SIAM, 2007.

M. J. Hadley. Web application description language (wadl). Technical report, Mountain View,

CA, USA, 2006.

J. Han. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., San Francisco,

CA, USA, 2005.

O. Hatzi, G. Batistatos, M. Nikolaidou, and D. Anagnostopoulos. A specialized search engine for

web service discovery. In C. A. Goble, P. P. Chen, and J. Zhang, editors, ICWS, pages 448{455.

IEEE, 2012.

N. Jardine and C. J. van Rijsbergen. The use of hierarchical clustering in information retrieval.

Information Storage and Retrieval, 7:217{240, 1971.

L. Kaufman and P. J. Rousseeuw. Finding groups in data: an introduction to cluster analysis.

John Wiley and Sons, New York, 1990.

R. Khare and T. C elik. Microformats: a pragmatic path to the semantic web. In WWW '06:

Proceedings of the 15th international conference on World Wide Web, pages 865{866, New York,

NY, USA, 2006. ACM.

M. Klusch and P. Kapahnke. Owl-s service retrieval test collection, February 2005.

J. Kopecky, T. Vitvar, C. Bournez, and J. Farrell. Sawsdl: Semantic annotations for wsdl and xml

schema. IEEE Internet Computing, 11(6):60{67, Nov. 2007.

J. Ma, Y. Zhang, and J. He. Eciently nding web services using a clustering semantic approach.

In Proceedings of the 2008 International Workshop on Context Enabled Source and Service Selec-

tion, Integration and Adaptation: Organized with the 17th International World Wide Web Con-

ference (WWW 2008), CSSSIA '08, pages 5:1{5:8, New York, NY, USA, 2008. ACM.

C. D. Manning, P. Raghavan, and H. Schutze. Introduction to Information Retrieval. Cambridge

University Press, New York, NY, USA, 2008.

D. Martin, M. Burstein, J. Hobbs, O. Lassila, D. McDermott, S. McIlraith, S. Narayanan,

M. Paolucci, B. Parsia, T. Payne, E. Sirin, N. Srinivasan, and K. Sycara. Owl-s: Semantic markup

for web services. Internet [http://www.w3.org/Submission/2004/SUBM- OWL-S-20041122/],

M. Paolucci, T. Kawamura, T. R. Payne, and K. P. Sycara. Semantic matching of web services

capabilities. In Proceedings of the First International Semantic Web Conference on The Semantic

Web, ISWC '02, pages 333{347, London, UK, UK, 2002. Springer-Verlag.

D. Roman, U. Keller, H. Lausen, J. de Bruijn, R. Lara, M. Stollberg, A. Polleres, C. Feier,

C. Bussler, and D. Fensel. Web service modeling ontology. Appl. Ontol., 1(1):77{106, Jan. 2005.

P. Rousseeuw. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis.

J. Comput. Appl. Math., 20(1):53{65, Nov. 1987.

J. J. Samper, F. J. Adell, L. van den Berg, and J. J. Martinez. Improving semantic web service

discovery. In Journal of Networks, 2008.

K. Seidler and K. Vogelgesang. Apache friends xampp, February 2013.

M. K. W. Abramowicz, K. Haniewicz and D. Zyskowski. Design of web services ltering and

clustering system. International Journal On Advances in Internet Technology, 1(1), 2008.

X. Wang, Z. Wang, and X. Xu. Semi-empirical service composition: A clustering based approach.

In ICWS, pages 219{226. IEEE Computer Society, 2011.

J. Wu, L. Chen, Z. Zheng, M. R. Lyu, and Z. Wu. Clustering web services to facilitate service

discovery. Knowl. Inf. Syst., 38(1):207{229, 2014.

Y. Xia, P. Chen, L. Bao, M. Wang, and J. Y. 0001. A qos-aware web service selection algorithm

based on clustering. In ICWS, pages 428{435. IEEE Computer Society, 2011.

R. Xu and D. Wunsch, II. Survey of clustering algorithms. Trans. Neur. Netw., 16(3):645{678,

May 2005.

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2015-03-30

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