Abstract
Web service tags, terms annotated by users to describe the functionality or other aspects of Web services, are being treated as collective user knowledge for Web service mining. However, the tags associated with a Web service generally are listed in a random order or chronological order without considering the relevance information, which limits the effectiveness of tagging data. In this paper, we propose a novel tag ranking approach to automatically rank tags according to their relevance to the target Web service. In particular, service-tag network information is utilized to compute the relevance scores of tags by employing HITS model. Furthermore, we apply tag ranking approach in Web service clustering. Comprehensive experiments based on 15,968 real Web services demonstrate the effectiveness of the proposed tag ranking approach.
Chapter PDF
Similar content being viewed by others
References
Ames, M., Naaman, M.: Why we tag: Motivations for annotation in mobile and online media. In: Proc. of the SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 971–980 (2007)
Arvelin, K.J., Kekalainen, J.: Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems 20(4), 422–446 (2002)
Averbakh, A., Krause, D., Skoutas, D.: Exploiting User Feedback to Improve Semantic Web Service Discovery. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 33–48. Springer, Heidelberg (2009)
Azmeh, Z., Falleri, J.-R., Huchard, M., Tibermacine, C.: Automatic Web Service Tagging Using Machine Learning and WordNet Synsets. In: Filipe, J., Cordeiro, J. (eds.) WEBIST 2010. LNBIP, vol. 75, pp. 46–59. Springer, Heidelberg (2011)
Bouillet, E., Feblowitz, M., Feng, H., Liu, Z., Ranganathan, A., Riabov, A.: A folksonomy-based model of web services for discovery and automatic composition. In: IEEE International Conference on Services Computing, pp. 389–396 (2008)
Chen, L., Hu, L., Zheng, Z., Wu, J., Yin, J., Li, Y., Deng, S.: WTCluster: Utilizing Tags for Web Services Clustering. In: Kappel, G., Maamar, Z., Motahari-Nezhad, H.R. (eds.) ICSOC 2011. LNCS, vol. 7084, pp. 204–218. Springer, Heidelberg (2011)
Ding, Z., Lei, D., Yan, J., Bin, Z., Lun, A.: A web service discovery method based on tag. In: International Conference on Complex, Intelligent and Software Intensive Systems, pp. 404–408 (2010)
George, Z., Athman, B.: Web service mining. Springer (2010)
Hou, J., Zhang, J., Nayak, R., Bose, A.: Semantics-Based Web Service Discovery Using Information Retrieval Techniques. In: Geva, S., Kamps, J., Schenkel, R., Trotman, A. (eds.) INEX 2010. LNCS, vol. 6932, pp. 336–346. Springer, Heidelberg (2011)
Kennedy, L.S., Chang, S.F., Kozintsev, I.V.: To search or to label?: predicting the performance of search-based automatic image classifiers. In: Proc. of the 8th ACM International Workshop on Multimedia Information Retrieval, pp. 249–258 (2006)
Li, L., Shang, Y., Zhang, W.: Improvement of hits-based algorithms on web documents. In: Proc. of the 11th International World Wide Web Conference, pp. 527–535 (2002)
Sigurbjörnsson, B., van Zwol, R.: Flickr tag recommendation based on collective knowledge. In: Proc. of the 17th International Conference on World Wide Web (WWW), pp. 327–336 (2008)
Zheng, Z., Ma, H., Lyu, M.R., King, I.: Wsrec: A collaborative filtering based web service recommender system. In: Proc. of the 7th International Conference on Web Services (ICWS), pp. 437–444 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, L., Zheng, Z., Feng, Y., Wu, J., Lyu, M.R. (2012). WSTRank: Ranking Tags to Facilitate Web Service Mining. In: Liu, C., Ludwig, H., Toumani, F., Yu, Q. (eds) Service-Oriented Computing. ICSOC 2012. Lecture Notes in Computer Science, vol 7636. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34321-6_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-34321-6_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34320-9
Online ISBN: 978-3-642-34321-6
eBook Packages: Computer ScienceComputer Science (R0)