NDLI: A semantic matching method for clustering traders in B2B systems

Content Provider	IEEE Xplore Digital Library
Author	Tomaz, R.F. Labidi, S. Wanghon, B.
Copyright Year	2003
Description	Author affiliation: Intelligent Syst. Lab., Federal Univ. of Maranhao, Sao Luis, Brazil (Tomaz, R.F.; Labidi, S.; Wanghon, B.)
Abstract	This is a part of the ICS (intelligent commerce system) project whose aim is to design and implement an effective intelligent B2B commerce system. We present a practical application on semantic Web and Web services concepts where a matching procedure to associate potential buyers and suppliers in a B2B system is shown and the use of patterns and tools in the development of semantic Web's structure is proposed as an alternative for its implementation.
Sponsorship	Dept. of Comput. Sci. of the Univ. of Chile The Millennium Sci. Initiative of the Chilean Minist. of Planning The Chilean Fulbright Comm. Sun Microsystems
Starting Page	144
Ending Page	153
File Size	327515
Page Count	10
File Format	PDF
ISBN	0769520588
DOI	10.1109/LAWEB.2003.1250292
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-11-12
Publisher Place	Chile
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Semantic Web Web services Databases Mobile agents Ontologies Intelligent systems Environmental economics Assembly Pattern matching Business
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
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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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A semantic matching method for clustering traders in B2B systems