• KAILASH CHANDER BHARDWAJ Department of Computer Science and Engineering Thapar University, Patiala- 147004, India
  • R.K. SHARMA School of Mathematics and Computer Applications Thapar University, Patiala-147004. India
  • R.K. SHARMA School of Mathematics and Computer Applications Thapar University, Patiala-147004. India


Semantics, web ontology language, web service description language, web services modeling language, web service modeling ontology, neural networks, fuzzy logic, ontology, quality of services


The web service discovery mechanism has continuously evolved during the last years. There is plethora of information available about various techniques and methods used for meeting the challenge of improving web service discovery. A tremendous effort has been reported in literature and researchers are still contributing to make the web service discovery more effective and efficient. This paper discusses various eminent researchers’ work in this direction using machine learning based techniques. Machine learning is a promising area for researchers to produce accurate estimates consistently. Machine learning system effectively “learns” how to estimate from training set of completed projects. We hope that this paper would benefit researchers to carry further work discussed in this paper and provide an outlook for the future research trends.



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Paolucci, M., Kawamura, T., Payne, T. R., Sycara, K.: Importing the semantic web in UDDI. In

proceedings of the International Workshop on Web Services, E-Business, and the Semantic Web.

Toronto, May 27-28, pp. 225-236, 2002.

Wang, Y., Stroulia, E.: Flexible interface matching for web service discovery. In proceedings of

the Fourth International Conference on Service Oriented Computing (WISE’03), Italy, IEEE

Computer Society Press, pp. 147-156, 2003.

Jiangang, M., Yanchun, Z., Jing, H.: Efficiently finding web services using a clustering semantic

approach. In proceedings of the International workshop on context enabled source and service

selection, integration and adaptation, 2008.

Gilbert, C., Payam, B., Klaus, M.: Probabilistic methods for service clustering. In proceedings of

International Semantic Web Conference (ISWC), 2010.

Nayak, R., Lee, B.: Web service discovery with additional semantics and clustering. In

proceedings of IEEE/WIC/ACM International Conference on Web Intelligence, pp. 555-558,

Ying, L.: Algorithm for semantic web services clustering and discovery. In proceedings of

International Conference on Communications and Mobile Computing, 2010.

Ram, S., Hwang, Y. and Zhao H.: A clustering based approach for facilitating semantic web

service discovery. In proceedings of 15th Annual Workshop on Information Technologies &

Systems (WITS) Paper, 2006.

Elgazzar, K., and Hassan, A. E., Martin P.: Clustering WSDL documents to bootstrap the

discovery of web services. In proceedings of the 2010 IEEE International Conference on Web

Services, pp. 147-154, 2010.

Wei, L., Wilson W.: Web service clustering using text mining techniques. International Journal of

Agent Oriented Software Engineering, vol. 3, no. 1, pp. 6-26, 2009.

Ismaili, F., Zenuni, X., Raufi, B.: A novel approach for efficiently finding web services on the

web. In proceedings of the ITI 31st International Conference on Information Technology

Interfaces, 2009.

Al-Masri, E., Mahmoud, Q. H.: Discovering the best web service: A neural network based

solution. In proceedings of the IEEE International Conference on Systems, Man, and Cybernetics,

Zhou, J., Li, S.: Semantic web service discovery approach using service clustering. In proceedings

of International Conference on Information Engineering and Computer Science, pp. 1-5, 2009.

Zheng, G., Bouguettaya, A.: Service mining on the web. IEEE transactions on services computing,

vol. 2, no. 1, 2009.

Zhou, J., Zhang, T., Hui, M., Xiao, L., Chen, G., Li, D.: Web service discovery based on keyword

clustering and ontology. In proceedings of IEEE International Conference on Granular

Computing, vol. 1, pp. 844-848, 2008.

Lu, H.: Semantic web services discovery and ranking. In proceedings of the IEEE/WIC/ACM

international conference on web intelligence, 2005.

Shehzad, K., Javed, M. Y.: Multithreaded fuzzy logic based web services mining framework.

European Journal of Scientific Research, vol. 41, no. 4, pp. 632-644, 2010.

Su, Z., Chen, H., Zhu, L., Zeng, Y.: Framework of semantic web service discovery based on fuzzy

logic and multi-phase matching”, Journal of Information & Computational Science vol. 9 , no. 1,

pp. 203- 214, 2012.

Ramu, G., Dr Reddy, B. E.: A framework for semantic web mining model. International Journal

of Internet Computing, vol. 1, no. 1, pp. 94-98, 2011.

Huang, A.: Similarity measures for text document clustering. In proceedings of the New Zealand

Computer Science Research Student Conference, pp. 49-56, 2008.

Chandramohan, D., Khapre, S., Ashokkumar, S.: A study of finding similarities in web service

using metrics. International Journal of Scientific & Engineering Research, vol. 2, no. 6, 2011.

Murali, K. S., Durga, B. S.: An efficient approach for text clustering based on frequent item sets.

European Journal of Scientific Research ISSN 1450-216X, vol. 42, no. 3, pp. 399-410, 2010.

Adala, A., Tabbane, N., Tabbane, S.: A framework for automatic web service discovery based on

semantics and NLP techniques. Advanced in multimedia-special issue on web services in

multimedia communication, vol. 2011, no. 1, 2011.

Feng, X., Wua, G.-F.: A cluster algorithm for QOS-oriented supply and demand discovery. In

proceedings of SIGSVC Workshop, Sprouts:Working Papers on Information Systems, 2011.

Dorn, C., Dustdar, S.: Weighted fuzzy clustering for capability-driven service aggregation. IEEE

International Conference on Service-Oriented Computing and Applications, pp. 1-8, 2010.

Sharma, S., and Batra, S.: Applying association rules for web services categorization. In

proceedings of International Journal of Computer and Electrical Engineering, vol. 2, no. 3, pp.

-468, 2010.

Huang, C.-L., Lo, C.-C., Chao, K.-M., Younas, M.: Reaching consensus: A moderated fuzzy web

services discovery method. Information & Software Technology vol. 48, no. 6, pp. 410-423, 2006.

Nayak, R., Tong, C.: Applications of data mining in web services. In proceedings of the 5th

International Conferences on Web Information Systems, Brisbane, Australia, pp. 199-205, 2004.

Abramowicz, W., Haniewicz, K., Kaczmarek, M., Zyskowski, D.: Architecture for web services

filtering and clustering. In proceedings of International Conference on Internet and Web

Applications and Services, 2007.

Sotolongo, R., Kobashikawa, C., Dong, F., Hirota, K.: Algorithm for web service discovery based

on information retrieval using WordNet and linear discriminant functions. Journal of Advanced

Computational Intelligence and Intelligent Informatics, vol. 12, pp-182-183, 2008.

Web Services Architecture,

Makripoulias, Y., Makris, C., Panagis, Y., Sakkopoulos, E., Adamopoulou, P., and Tsakalidis, A.:

Web service discovery based on quality of service, In proceedings of IEEE International

Conference on Computer Systems and Applications, pp. 196-199, 2006.

Qianhui, L., Jen-Yao, C., Steven, M., Steven, O.: Service pattern discovery of web service mining

in web service registry-repository. In proceedings of IEEE International Conference on e-Business

Engineering, 2006.

Gholamzadeh, N., and Taghiyareh: Ontology based fuzzy web services clustering. In proceedings

of International Symposium on Telecommunications, pp. 721-725, 2010.

Gao, H., Stucky, W.: Web services based intelligent clustering techniques. In proceedings of IEEE

international forum on information technology and applications, pp. 242- 245, 2009.

Wang, H., Shi, Y., Zhouy, X., Zhou, Q.: Web service classification using support vector machine.

In proceedings of IEEE 22nd International Conference on Tools with Artificial Intelligence,

Jamous, M. M., Safaai, B. D.: Web services non-functional classification to enhance discovery

speed. International Journal of Computer Science Issues, vol. 8, no. 4, 2011.

Mohanty, R., Ravi, V., Patra, M. R.: Classification of Web Services Using Bayesian Network.

Journal of Software Engineering and Applications, vol. 5, no. 4, pp. 291-296, 2012.

Satish, N., Wahidabanu, R. S. D.: Web service mining based on annotated capability

specifications. European Journal of Scientific Research, vol. 69, no.3, pp. 416-427, 2012.

Paik, I., Fujikawa, E.: Web service matchmaking using web search engine and machine learning.

International Journal of Web Engineering vol. 1, no. 1, pp. 1-5, 2012.

Yuan-jie, L., Jian, C.: Web service classification based on automatic semantic annotation and

ensemble learning. In proceeding of IEEE 26th International Parallel and Distributed Processing

Symposium Workshops & PhD Forum, pp. 2274-79, 2012.

Shafiq, O., Alhajj, R., Rokne, J. G., Toma, I.: Light-weight semantics and bayesian classification:

A hybrid technique for dynamic web service discovery. In proceeding of IEEE International

conference on Information Reuse and Integration, pp. 121-125, 2010.

Crasso, M., Zunino, A., Campo, M.:AWSC: An approach to web services classification based on

machine learning techniques. CONICET, Inteligencia Artificial, Revista Iberoamericana de

Inteligencia Artificial. no. 37, pp. 25-36, 2008.

Kuang, L., wu, J., Deng, S.: Service classification using adaptive back-propagation neural network

and semantic similarity. In proceedings of the 10th International Conference on Computer

Supported Cooperative Work in Design, 2006.

Bruno, M., Canfora, G.: Using SVM and concept analysis to support web service classification and

annotation. In proceedings of IEEE international conference on e-Technology, e- Commerce and

e-Service, 2005.

Bennaceur, A., Issarny, V., Johansson, R., Moschitti, A., Sykes, D.: MachinelLearning for

automatic classification of web service interface descriptions. In ISoLA 2011 Workshops, pp.

–231, 2012.

Oldham, N., Thomas, C., Sheth, A., Verma, K.: METEOR-S web service annotation framework

with machine learning classification. In proceedings of the 13th international conference on

World Wide Web, pp. 553-562, 2004.

Banage, T., Kumara, G. S., Incheon, P., Wuhui, C.: Web-service clustering with a hybrid of

ontology learning and information-retrieval-based term similarity. IEEE 20th International

Conference on Web Services, 2013.

Erl, T.: Service-Oriented Architecture: A field guide to integrating XML and web services,

Prentice Hall, 2004.

Josuttis, N.M.: SOA in Practice: The art of distributed system design. Publisher: O'Reilly Media,

Bellwood, T., Clement, L., Riegen, C.V., Uddi version 3.0.1. v3.htm.

World Wide Web Consortium (W3C): Web Services Description Language (WSDL)1.1.

Fensel, D., Lausen, H., Polleres, A., Bruijn, J., Stollberg, M., Roman, D., and Domingue, J.:

Enabling semantic web services: the web service modeling ontology, Springer, 2007.

Bruijn, J. d.: The web service modeling language WSML, WSMO final draft v0.2, 2005.

Sahami, M., Dumais, S., Heckerman, D., Horvitz, E.: A Bayesian approach to filtering junk email,

learning for text

categorization, pp. 55-62, AAAI Workshop, 1998.

Vapnik, V. N., Druck, H., Wu, D.: Support vector machines for spam categorization, IEEE Trans.

on Neural Networks, 10(5), pp. 1048-1054, 1999.

Wang, Y.Q.: Theory and method of artificial intelligence. Press of Xi’an JiaoTong University,

Quinlan, J. R.: Induction of Decision Trees. Machine Learning 1: 81-106, Kluwer Academic

Publishers, 1986.

Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large

databases. In proceedings of the 1993 ACM SIGMOD international conference on Management of

data - SIGMOD, 1993.

Pudil, P., Novovicova, J., Kittler, J.: Automatic machine learning of decision rules for

classification problems. In proceedings of 4th BMVC93, 1993.

Varguez-Moo M., Moo-Mena, F., and Uc-Cetina, V.: Use of classification algorithms for semantic

web services discovery. Journal of Computers, vol. 8, no. 7, pp. 1810- 1814, 2013.

Sonawani, S., and Mukhopadhyay., D.: A decision tree approach to classify web services using

quality parameters, In proceedings of International Conference on Web Engineering and

Application, ICWA, 2013.

XMethods,, accessed February 2012., Accessed February 2012, Remote Methods: Home of Web


Web Service List, , Accessed February 2012.

Swami Das M., Mohanty, R., Vijayalakshmi, D., Govardhan, A. : Application of data mining

using Bayesian belief

network to classify quality of web services. Special Issue of International Journal of Computer

Science & Informatics (IJCSI), ISSN (PRINT) : 2231–5292, vol. II, no. 1, 2008.

Heß, A., Kushmerick, N.: Machine learning for annotating semantic web services. In proceedings

of AAAI Spring Symposium on Semantic Web Services, 2004.

Saha, S., Murthy, C.A., Pal, S.K.: Classification of web services using tensor space model and

rough ensemble classifier. In Proceedings of 17th international Symposium on Foundations of

Intelligent Systems (ISMIS’08), pp. 508–

Toronto, Canada, 2008.

Kritikos, K., Plexousakis, D.: Semantic QoS-based web service discovery algorithms. In

Proceedings of the 5th IEEE European Conference on Web Services, ECOWS 07, 2007.