Presentation 2013-03-15
Calculating Word Similarity for Context Aware Web Service Clustering
Hiroki Ohashi, Incheon Paik,
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Abstract(in English) Web service discovery is becoming a challenging and time consuming task due to large number of Web services available on the Internet. Organizing the Web services into functionally similar clusters is one of a very efficient approach for reducing the search space. To cluster Web services, we first extract the features (e.g., service name)to measure the similarities from service descriptions from Web or registry. The services usually consist of complex terms, from which we can get service features in some contexts. Current works for service clustering have not considered the context. To make clustering of web services by domain context, we need calculation of terms similarity under a specific context. In this paper, we suggest a novel method to measure terms similarity consider the specific domain context using machine learning.
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Keyword(in English) Web Service / Word Similarity / Clustering
Paper # SC2012-22
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Committee SC
Conference Date 2013/3/8(1days)
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Registration To Services Computing (SC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Calculating Word Similarity for Context Aware Web Service Clustering
Sub Title (in English)
Keyword(1) Web Service
Keyword(2) Word Similarity
Keyword(3) Clustering
1st Author's Name Hiroki Ohashi
1st Author's Affiliation Graduate School of Computer Science and Engineering, University of Aizu()
2nd Author's Name Incheon Paik
2nd Author's Affiliation Graduate School of Computer Science and Engineering, University of Aizu
Date 2013-03-15
Paper # SC2012-22
Volume (vol) vol.112
Number (no) 497
Page pp.pp.-
#Pages 3
Date of Issue