Abstract
Semantic Web services have emerged as the solution to the need for automating several aspects related to service-oriented architectures, such as service discovery and composition, and they are realized by combining Semantic Web technologies and Web service standards. In the present paper, we tackle the problem of automated classification of Web services according to their application domain taking into account both the textual description and the semantic annotations of OWL-S advertisements. We present results that we obtained by applying machine learning algorithms on textual and semantic descriptions separately and we propose methods for increasing the overall classification accuracy through an extended feature vector and an ensemble of classifiers.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Aha, D., Kibler, D.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)
Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F.: The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press (2003)
Bruno, M., Canfora, G., Penta, M.D., Scognamiglio, R.: An approach to support web service classification and annotation. In: Proceedings IEEE International Conference on e-Technology, e-Commerce and e-Service, pp. 138–143. Washington, DC (2005). DOI http://dx.doi.org/10.1109/EEE.2005.31
Cohen, W.W.: Fast effective rule induction. In: Proceedings 12th International Conference on Machine Learning, pp. 115–123 (1995)
Corella, M., Castells, P.: Semi-automatic semantic-based web service classification. In: J. Eder, S. Dustdar (eds.) Business Process Mangement Workshops, Springer Verlag Lecture Notes in Computer Science, vol. 4103, pp. 459–470. Vienna, Austria (2006)
Heβ, A., Johnston, E., Kushmerick, N.: ASSAM: A tool for semi-automatically annotating semantic web services. In: Proceedings 3rd International Semantic Web Conference (2004)
Hess, A., Kushmerick, N.: Learning to attach semantic metadata to web services. In: Proceedings International Semantic Web Conference (ISWC'03), pp. 258–273 (2003)
John, G.H., Langley, P.: Estimating continuous distributions in bayesian classifiers. In: Proceedings 11th Conference on Uncertainty in Artificial Intelligence, pp. 338–345. Morgan Kaufmann, San Mateo (1995)
Kiefer, C., Bernstein, A.: The creation and evaluation of isparql strategies for matchmaking. In: M. Hauswirth, M. Koubarakis, S. Bechhofer (eds.) Proceedings 5th European Semantic Web Conference, LNCS. Springer Verlag, Berlin, Heidelberg (2008). URL http://data.semanticweb.org/conference/eswc/2008/papers/133
Klusch, M., Kapahnke, P., Fries, B.: Hybrid semantic web service retrieval: A case study with OWLS-MX. In: International Conference on Semantic Computing, pp. 323–330. IEEE Computer Society, Los Alamitos, CA (2008). DOI http://doi.ieeecomputersociety.org/10.1109/ICSC.2008.20
Kopecký, J., Vitvar, T., Bournez, C., Farrell, J.: Sawsdl: Semantic annotations for wsdl and xml schema. IEEE Internet Computing 11(6), 60–67 (2007). DOI http://dx.doi.org/10.1109/MIC.2007.134
Meditskos, G., Bassiliades, N.: Object-oriented similarity measures for semantic web service matchmaking. In: Proceedings 5th IEEE European Conference on Web Services (ECOWS'07), pp. 57–66. Halle (Saale), Germany (2007)
Oldham, N., Thomas, C., Sheth, A., Verma, K.: Meteor-s web service annotation framework with machine learning classification. In: Proceedings 1st International Workshop on Semantic Web Services and Web Process Composition (SWSWPC'04), pp. 137–146 (2005)
Paolucci, M., Kawamura, T., Payne, T.R., Sycara, K.P.: Importing the semantic web in uddi. In: Revised Papers from the International Workshop on Web Services, E-Business, and the Semantic Web (CAiSE'02/WES'02), pp. 225–236. Springer-Verlag, London, UK (2002)
Paolucci, M., Kawamura, T., Payne, T.R., Sycara, K.P.: Semantic matching of web services capabilities. In: Proceedings 1st International Semantic Web Conference on The Semantic Web (ISWC'02), pp. 333–347. Springer-Verlag, London, UK (2002)
Platt, J.: Machines using sequential minimal optimization. In: B. Schoelkopf, C. Burges, A. Smola (eds.) Advances in Kernel Methods - Support Vector Learning. MIT Press (1998). URL http://research.microsoft.com/jplatt/smo.html
Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Ma-teo, CA (1993)
Saha, S., Murthy, C.A., Pal, S.K.: Classification of web services using tensor space model and rough ensemble classifier. In: Proceedings 17th International Symposiumon Foundations of Intelligent Systems (ISMIS'08), pp. 508–513. Toronto, Canada (2008)
Sirin, E., Parsia, B., Grau, B.C., Kalyanpur, A., Katz, Y.: Pellet: A practical owl-dl reasoner. Web Semant. 5(2), 51–53 (2007). DOI http://dx.doi.org/10.1016/j.websem.2007.03.004
Tsoumakas, G., Katakis, I.: Multi-label classification: An overview. International Journal of Data Warehousing and Mining 3(3), 1–13 (2007)
Vapnik, V.N.: The nature of statistical learning theory. Springer-Verlag, NY, USA (1995)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd Edition. Morgan Kaufmann Publishers Inc., San Francisco, CA (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 IFIP International Federation for Information Processing
About this paper
Cite this paper
Katakis, I., Meditskos, G., Tsoumakas, G., Bassiliades, N., Vlahavas (2009). On the Combination of Textual and Semantic Descriptions for Automated Semantic Web Service Classification. In: Iliadis, Maglogiann, Tsoumakasis, Vlahavas, Bramer (eds) Artificial Intelligence Applications and Innovations III. AIAI 2009. IFIP International Federation for Information Processing, vol 296. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-0221-4_13
Download citation
DOI: https://doi.org/10.1007/978-1-4419-0221-4_13
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-0220-7
Online ISBN: 978-1-4419-0221-4
eBook Packages: Computer ScienceComputer Science (R0)