Abstract
We argue that the automatic classification of web service interface descriptions into a predefined set of categories can considerably speed up the task of finding compatible web services. By doing so, we restrict computationally-expensive compatibility checking to systems within the same domain category. In this paper we show that this classification can be carried out by leveraging techniques derived from automatic document classification. In particular, we devise an approach that exploit the characteristics of web service interface descriptions to extract the features necessary for inferring the categorisation function. We further reports the results of experiments in categorising various web service interface descriptions using different classification algorithms.
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References
Basili, R., Moschitti, A.: Automatic Text Categorization: from Information Retrieval to Support Vector Learning. Aracne editrice, Rome (2005)
Bennaceur, A., Blair, G.S., Chauvel, F., Georgantas, N., Grace, P., Nundloll, V., Paolucci, M., Saadi, R., Sykes, D.: Intermediate connect architecture. Technical Report D1.2, Connect ICT FET IP Project (February 2011)
Boser, B.E., Guyon, I., Vapnik, V.: A training algorithm for optimal margin classifiers. In: Computational Learning Theory, Pittsburgh, United States, pp. 144–152 (1992)
Calvert, K.L., Lam, S.S.: Formal methods for protocol conversion. IEEE Journal on Selected Areas in Comm. (1990)
Crammer, K., Dekel, O., Keshet, J., Shalev-Schwartz, S., Singer, Y.: Online passive-aggressive algorithms. Journal of Machine Learning Research 7, 551–585 (2006)
Crammer, K., Singer, Y.: On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research 2, 265–585 (2001)
Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., Lin, C.-J.: Liblinear: A library for large linear classification. Journal of Machine Learning Research 9, 1871–1874 (2008)
Heß, A., Kushmerick, N.: Learning to Attach Semantic Metadata to Web Services. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 258–273. Springer, Heidelberg (2003)
Joachims, T.: Learning to Classify Text using Support Vector Machines. Kluwer/Springer, Boston (2002)
Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: RCV1: A new benchmark collection for text categorization research. Journal of Machine Learning Research 5, 361–397 (2004)
Mitchell, T.M.: Machine Learning. McGraw-Hill (1997)
Moschitti, A.: Kernel methods, syntax and semantics for relational text categorization. In: Proceedings of ACM 17th Conference on Information and Knowledge Management (CIKM), Napa Valley, United States (2008)
Moschitti, A., Basili, R.: Complex Linguistic Features for Text Classification: A Comprehensive Study. In: McDonald, S., Tait, J.I. (eds.) ECIR 2004. LNCS, vol. 2997, pp. 181–196. Springer, Heidelberg (2004)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of EMNLP (2002)
Ranganathan, S.R.: Colon Classification. Ess Ess Publications, Delhi (2006)
Rosenblatt, F.: The Perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review 65(5), 386–408 (1958)
Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Technical Report TR74-218, Department of Computer Science, Cornell University, Ithaca, New York (1974)
Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press (2002)
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Bennaceur, A., Issarny, V., Johansson, R., Moschitti, A., Sykes, D., Spalazzese, R. (2012). Machine Learning for Automatic Classification of Web Service Interface Descriptions. In: Hähnle, R., Knoop, J., Margaria, T., Schreiner, D., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification, and Validation. ISoLA 2011. Communications in Computer and Information Science, vol 336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34781-8_17
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DOI: https://doi.org/10.1007/978-3-642-34781-8_17
Publisher Name: Springer, Berlin, Heidelberg
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