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Abstract
Introduction
Related Work
Conclusion and Perspectives
References

Towards Service Ontology for Web Services Storage and Discovery

Profile image of Moussa KaouanMoussa KaouanProfile image of Djelloul BouchihaDjelloul Bouchiha

2020, Towards Service Ontology for Web services storage and discovery

Abstract

The ontology has marked its presence in several research fields in order to address issues in knowledge mod-eling, Artificial Intelligence, classification, clustering and more specifically in knowledge engineering. In the Web Service field, ontology is used principally to describe the Web Service interface for discovering, storing and composing Web services. This is well known as semantic Web services, such as OWLS , WSMO and WSDL-S. Unfortunately, work in this field remain insufficient till now; so, supporting this description by adding a shared ontology (knowledge base) can improve the performance of the Web Service technology. Manual construction of ontology is a tedious and expensive task which requires domain expert intervention; an automatic ontological construction process will be interesting and less costly solution in terms of time and money. In this paper, we propose to use clustering algorithms to create an ontological network (Service Ontology (SO)) from a semantic Web Services corpus. The created SO can be used for storage and discovery of Web Services in a distributed and intelligent environment.

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References (39)

  1. F. Emekci, O. D. Sahin, D. Agrawal, and A. El Abbadi, "A peer-to-peer framework for web service discovery with ranking," in Proceedings. IEEE International Conference on Web Services, 2004. IEEE, 2004, pp. 192-199.
  2. O. D. Sahin, C. E. Gerede, D. Agrawal, A. El Abbadi, O. Ibarra, and J. Su, "Spider: P2p-based web service discovery," in International Conference on Service-Oriented Computing. Springer, 2005, pp. 157- 169.
  3. C. Pennington, "Introduction to web services," in Semantic Web Ser- vices: Theory, Tools and Applications. IGI Global, 2007, pp. 134-154.
  4. M. Kaouan, D. Bouchiha, S. M. Benslimane, and S. B. Hacene, "Ontology-based web services classification for registration and discov- ery of web services," International Journal of Artificial Intelligence and Soft Computing, vol. 6, no. 2, pp. 129-147, 2017.
  5. T. R. Gruber, "A translation approach to portable ontology specifica- tions," Knowledge acquisition, vol. 5, no. 2, pp. 199-220, 1993.
  6. S. Vinoski, "Distributed object computing with corba," C++ Report, vol. 5, no. 6, pp. 32-38, 1993.
  7. G. Glass, "The web services (r) evolution. applying web services to applications," IBM Corp, 2000.
  8. J. Roy and A. Ramanujan, "Understanding web services," IT profes- sional, vol. 3, no. 6, pp. 69-73, 2001.
  9. M. Paolucci, T. Kawamura, T. R. Payne, and K. Sycara, "Importing the semantic web in uddi," in International Workshop on web services, e-business, and the semantic web. Springer, 2002, pp. 225-236.
  10. --, "Semantic matching of web services capabilities," in International semantic web conference. Springer, 2002, pp. 333-347.
  11. S. Singh and M. S. Aswal, "Ontology learning procedures based on web mining techniques," in International Conference on Advances in Engineering Science Management & Technology (ICAESMT)-2019, Uttaranchal University, Dehradun, India, 2019.
  12. Y. Cao, J. Liu, B. Cao, M. Shi, Y. Wen, and Z. Peng, "Web services classification with topical attention based bi-lstm," in International Conference on Collaborative Computing: Networking, Applications and Worksharing. Springer, 2019, pp. 394-407.
  13. C. Sánchez-Sánchez and L. B. Sheremetov, "N-gram representation for web service description classification," in International Conference on Machine Learning, Optimization, and Data Science. Springer, 2018, pp. 447-459.
  14. R. Nayak and B. Lee, "Web service discovery with additional semantics and clustering," in IEEE/WIC/ACM International Conference on Web Intelligence (WI'07). IEEE, 2007, pp. 555-558.
  15. M. Paolucci, N. Srinivasan, K. P. Sycara, and T. Nishimura, "Towards a semantic choreography of web services: from wsdl to daml-s." in ICWS, 2003, pp. 22-26.
  16. G. Karypis, E.-H. Han, and V. Kumar, "Chameleon: Hierarchical clus- tering using dynamic modeling," Computer, vol. 32, no. 8, pp. 68-75, 1999.
  17. A. Funk and K. Bontcheva, "Ontology-based categorization of web services with machine learning." in LREC, 2010.
  18. Q. Liang, P. Li, P. C. Hung, and X. Wu, "Clustering web services for automatic categorization," in 2009 IEEE International Conference on Services Computing. IEEE, 2009, pp. 380-387.
  19. Q. A. Liang, H. Lam, L. Narupiyakul, and P. C. Hung, "A rule-based approach for availability of web service," in 2008 IEEE International Conference on Web Services. IEEE, 2008, pp. 153-160.
  20. J. MacQueen et al., "Some methods for classification and analysis of multivariate observations," in Proceedings of the fifth Berkeley sympo- sium on mathematical statistics and probability, vol. 1, no. 14. Oakland, CA, USA, 1967, pp. 281-297.
  21. M. S. G. Karypis, V. Kumar, and M. Steinbach, "A comparison of document clustering techniques," in TextMining Workshop at KDD2000 (May 2000), 2000.
  22. H. El Bouhissi and M. Malki, "Semantic web service ontology construc- tion: A reverse engineering approach," in 2014 IEEE/ACS 11th Inter- national Conference on Computer Systems and Applications (AICCSA). IEEE, 2014, pp. 161-168.
  23. W. Liu and W. Wong, "Web service clustering using text mining tech- niques," International Journal of Agent-Oriented Software Engineering, vol. 3, no. 1, pp. 6-26, 2009.
  24. W. Wong, W. Liu, and M. Bennamoun, "Tree-traversing ant algorithm for term clustering based on featureless similarities," Data Mining and Knowledge Discovery, vol. 15, no. 3, pp. 349-381, 2007.
  25. Y. Shen and F. Liu, "An approach for semantic web discovery using unsupervised learning algorithms," in Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health. Springer, 2019, pp. 56-72.
  26. D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent dirichlet allocation," Journal of machine Learning research, vol. 3, no. Jan, pp. 993-1022, 2003.
  27. M. Bravo, R. A. Mora-Gutiérrez, and L. F. Hoyos-Reyes, "Bio-inspired hybrid algorithm for web services clustering," in Advanced Analytics and Artificial Intelligence Applications. IntechOpen, 2019.
  28. E. Della Valle, D. Cerizza, I. Celino, A. Turati, H. Lausen, N. Steinmetz, M. Erdmann, and A. Funk, "Realizing service-finder: Web service discovery at web scale," in European Semantic Technology Conference (ESTC), Vienna, 2008.
  29. J. McCarthy, "Circumscriptiona form of non-monotonic reasoning," Artificial intelligence, vol. 13, no. 1-2, pp. 27-39, 1980.
  30. "Owls-tc v4," http:// projects.semwebcentral.org/frs/?group id=89& re- lease id=380., accessed: April 2017.
  31. P. Jaccard, "Distribution comparée de la flore alpine dans quelques régions des alpes occidentales et orientales," Bulletin de la Murithienne, no. 31, pp. 81-92, 1902.
  32. D. Steinley and M. J. Brusco, "Initializing k-means batch clustering: A critical evaluation of several techniques," Journal of Classification, vol. 24, no. 1, pp. 99-121, 2007.
  33. M. E. Celebi, H. A. Kingravi, and P. A. Vela, "A comparative study of efficient initialization methods for the k-means clustering algorithm," Expert systems with applications, vol. 40, no. 1, pp. 200-210, 2013.
  34. D. Steinley, "Local optima in k-means clustering: what you don't know may hurt you." Psychological methods, vol. 8, no. 3, p. 294, 2003.
  35. T. Su and J. Dy, "A deterministic method for initializing k-means clus- tering," in 16th IEEE International Conference on Tools with Artificial Intelligence. IEEE, 2004, pp. 784-786.
  36. M. Astrahan, "Speech analysis by clustering, or the hyperphoneme method," STANFORD UNIV CA DEPT OF COMPUTER SCIENCE, Tech. Rep., 1970.
  37. T. Pedersen, S. Patwardhan, J. Michelizzi et al., "Wordnet:: Similarity- measuring the relatedness of concepts." in AAAI, vol. 4, 2004, pp. 25-29.
  38. A. C. Mazari, H. Aliane, and Z. Alimazighi, "Automatic construction of ontology from arabic texts." in ICWIT, 2012, pp. 193-202.
  39. Y. Jung, J. Ryu, K.-m. Kim, and S.-H. Myaeng, "Automatic construction of a large-scale situation ontology by mining how-to instructions from the web," Web Semantics: Science, Services and Agents on the World Wide Web, vol. 8, no. 2-3, pp. 110-124, 2010.

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