Improvement of HITS-based algorithms on web documents
Proceedings of the 11th international conference on World Wide Web
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
To search or to label?: predicting the performance of search-based automatic image classifiers
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Why we tag: motivations for annotation in mobile and online media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
A Folksonomy-Based Model of Web Services for Discovery and Automatic Composition
SCC '08 Proceedings of the 2008 IEEE International Conference on Services Computing - Volume 1
WSRec: A Collaborative Filtering Based Web Service Recommender System
ICWS '09 Proceedings of the 2009 IEEE International Conference on Web Services
Exploiting User Feedback to Improve Semantic Web Service Discovery
ISWC '09 Proceedings of the 8th International Semantic Web Conference
A Web Service Discovery Method Based on Tag
CISIS '10 Proceedings of the 2010 International Conference on Complex, Intelligent and Software Intensive Systems
Semantics-based web service discovery using information retrieval techniques
INEX'10 Proceedings of the 9th international conference on Initiative for the evaluation of XML retrieval: comparative evaluation of focused retrieval
WTCluster: utilizing tags for web services clustering
ICSOC'11 Proceedings of the 9th international conference on Service-Oriented Computing
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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.