Authors:
Jean-Rémy Falleri
;
Zeina Azmeh
;
Marianne Huchard
and
Chouki Tibermacine
Affiliation:
LIRMM, CNRS and Montpellier II University, France
Keyword(s):
Tags, Web services, Text mining, Machine learning.
Related
Ontology
Subjects/Areas/Topics:
Internet Technology
;
Searching and Browsing
;
Web Information Systems and Technologies
;
Web Interfaces and Applications
;
Web Services and Web Engineering
Abstract:
With the increasing interest toward service-oriented architectures, the number of existing Web services is dramatically growing. Therefore, finding a particular service among this ever increasing number of services is becoming a time-consuming task. User tags or keywords have proven to be a useful technique to smooth browsing experience in large document collections. Some service search engines, like Seekda, already propose this kind of facility. Service tagging, which is a fairly tedious and error prone task, is done manually by the providers and the users of the services. In this paper we propose an approach that automatically extracts tags from Web service descriptions. It identifies a set of relevant tags extracted from a service description and leaves only to the users the task of assigning tags not present in this description. The proposed approach is validated on a corpus of 146 services extracted from Seekda.