NDLI: Ontology learning with complex data type for Web service clustering

Content Provider	IEEE Xplore Digital Library
Author	Kumara, B.T.G.S. Paik, I. Koswatte, K.R.C. Wuhui Chen
Copyright Year	2014
Description	Author affiliation: Sch. of Comput. Sci. & Eng., Univ. of Aizu, Aizu-Wakamatsu, Japan (Kumara, B.T.G.S.; Paik, I.; Koswatte, K.R.C.; Wuhui Chen)
Abstract	Clustering Web services into functionally similar clusters is a very efficient approach to service discovery. A principal issue for clustering is computing the semantic similarity between services. Current approaches use similarity-distance measurement methods such as keyword, information-retrieval or ontology based methods. These approaches have problems that include discovering semantic characteristics, loss of semantic information and a shortage of high-quality ontologies. Further, current clustering approaches are considered only have simple data types in services' input and output. However, services that published on the web have input/ output parameter of complex data type. In this research, we propose clustering approach that considers the simple type as well as complex data type in measuring the service similarity. We use hybrid term similarity method which we proposed in our previous work to measure the similarity. We capture the semantic pattern exist in complex data types and simple data types to improve the ontology learning method. Experimental results show our clustering approach which uses complex data types in measuring similarity works efficiently.
Starting Page	129
Ending Page	136
File Size	1278758
Page Count	8
File Format	PDF
ISBN	9781479945184
DOI	10.1109/CIDM.2014.7008658
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning systems Web services Semantics Ports (Computers) Web Service Clustering Ontologies Feature extraction Educational institutions Complex Data Type Ontology Learning
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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