Abstract
Service oriented architecture is a glue that allows web applications to work in collaboration. It has become a driving force for the service-oriented computing (SOC) paradigm. In heterogeneous environments the SOC paradigm uses web services as the basic building block to support low costs as well as easy and rapid composition of distributed applications. A web service exposes its interfaces using the Web Service Description Language (WSDL). A central repository called universal description, discovery and integration (UDDI) is used by service providers to publish and register their web services. UDDI registries are used by web service consumers to locate the web services they require and metadata associated with them. Manually analyzing WSDL documents is the best approach, but also most expensive. Work has been done on employing various approaches to automate the classification of web services. However, previous research has focused on using a single technique for classification. This research paper focuses on the classification of web services using a majority vote based classifier ensemble technique. The ensemble model overcomes the limitations of conventional techniques by employing the ensemble of three heterogeneous classifiers: Naïve Bayes, decision tree (J48), and Support Vector Machines. We applied tenfold cross-validation to test the efficiency of the model on a publicly available dataset consisting of 3738 real world web services categorized into 5 fields, which yielded an average accuracy of 92 %. The high accuracy is owed to two main factors, i.e., enhanced pre-processing with focused feature selection, and majority based ensemble classification.
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References
Bai P, Li P (2009) The improved naive Bayesian web text classification algorithm. Int Symp Comput Netw Multimed Technol 2009:1–4
Bhargava DN, Sharma G, Bhargava R, Mathuria M (2013) Decision tree analysis on j48 algorithm for data mining. Int J Adv Res Comput Sci Softw Eng 3(6):RAJ123
Bruno M, Canfora G, Penta MD, Scognamiglio R (2005) An approach to support web service classification and annotation. In: Proceedings of the 2005 IEEE international conference on e-technology, e-commerce and e-service. Washington, DC
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), pp 147–154
Elgedawy I (2014) USTA: an aspect-oriented knowledge management framework for reusable assets discovery. Arab J Sci Eng 40(2):451–474
Huang H, He L, Chen X, Yu M, Wang Z (2013) Automatic composition of heterogeneous models based on semantic web services. Int J Parallel Progr 43(3):339–358
Katakis I, Meditskos G, Tsoumakas G, Bassiliades N, Vlahavas IP (2009) On the combination of textual and semantic descriptions for automated semantic web service classification. In: Iliadis L, Maglogiannis I, Tsoumakas G, Vlahavas I, Bramer M (eds) Artificial intelligence applications and innovations III. Springer, New York, pp 95–104
Kouki J, Chainbi W, Ghedira K (2014) A novel framework for bindings synchronization of web services. Serv Oriented Comput Appl 9(1):59–74
Liu W, Wong W (2009) Web service clustering using text mining techniques. Int J Agent-Oriented Softw Eng 3(1):6–26
Liu ZZ, Jia ZP, Xue X, An JY (2014) Reliable web service composition based on QoS dynamic prediction. Soft Comput 19(5):1409–1425
Malki A, Benslimane D, Benslimane SM, Barhamgi M, Malki M, Ghodous P, Drira K (2015) Data services with uncertain and correlated semantics. World Wide Web. doi:10.1007/s11280-014-0317-x
Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge University Press, Cambridge
Martino Di (2009) Semantic web services discovery based on structural ontology matching. Int J Web Grid Serv. doi:10.1504/IJWGS.2009.023868
Patil AS, Pawar BV (2012) Automated classification of web sites using Naive Bayesian algorithm. In: Proceedings of the international multiconference of engineers and computer scientists
Plebani P, Pernici B (2009) URBE: web service retrieval based on similarity evaluation. IEEE Trans Knowl Data Eng 21(11):1629–1642
Rokach L, Schclar A, Itach E (2014) Ensemble methods for multi-label classification. Expert Syst Appl 41(16):7507–7523
Saha S, Murthy CA, Pal SK (2008) Classification of web services using tensor space model and rough ensemble classifier. In: Foundations of intelligent systems. Springer, Heidelberg, pp 508–513
Varguez-Moo M, Moo-Mena F, Uc-Cetina V (2013) Use of classification algorithms for semantic web services discovery. J Comput 8(7):1810–1814
Wang H, Shi Y, Zhou X, Zhou Q, Shao S, Bouguettaya A (2010) Web service classification using support vector machine. In: 22nd IEEE international conference on tools with artificial intelligence (ICTAI), vol 1, pp 3–6
Youquan H, Jianfang X, Cheng X (2011) An improved Naive Bayesian algorithm for web page text classification. In: Eighth international conference on fuzzy systems and knowledge discovery (FSKD), vol 3, pp 1765–1768
Zheng Z (2012) WS-DREAM. http://www.wsdream.net/dataset.html. Accessed 28 Jan 2015
Zhuang Z, Mitra P, Jaiswal A (2005) Corpus-based web services matchmaking. In: Workshop on exploring planning and scheduling for web services, grid and autonomic computing
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Accepted after three revisions by Prof. Dr. Suhl.
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Qamar, U., Niza, R., Bashir, S. et al. A Majority Vote Based Classifier Ensemble for Web Service Classification. Bus Inf Syst Eng 58, 249–259 (2016). https://doi.org/10.1007/s12599-015-0407-z
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DOI: https://doi.org/10.1007/s12599-015-0407-z