Hostname: page-component-74656f9884-8r8nv Total loading time: 0 Render date: 2026-11-09T02:51:36.308Z Has data issue: false hasContentIssue false

Context matching for electronic marketplaces: a case study

Published online by Cambridge University Press:  06 October 2004

MATTEO BONIFACIO
Affiliation:
ITC-IRST, Centro per la Ricerca Scientifica e Tecnologica, Panté di Povo, Trento, Italy. E-mail: antodona@itc.it, serafini@itc.it Department of Information Technology, University of Trento, Panté di Povo, Trento, Italy. E-mail: bonifacio@dit.unitn.it, molani@dit.unitn.it
ANTONIA DONÀ
Affiliation:
ITC-IRST, Centro per la Ricerca Scientifica e Tecnologica, Panté di Povo, Trento, Italy. E-mail: antodona@itc.it, serafini@itc.it
ALESSANDRA MOLANI
Affiliation:
ITC-IRST, Centro per la Ricerca Scientifica e Tecnologica, Panté di Povo, Trento, Italy. E-mail: antodona@itc.it, serafini@itc.it Department of Information Technology, University of Trento, Panté di Povo, Trento, Italy. E-mail: bonifacio@dit.unitn.it, molani@dit.unitn.it
LUCIANO SERAFINI
Affiliation:
ITC-IRST, Centro per la Ricerca Scientifica e Tecnologica, Panté di Povo, Trento, Italy. E-mail: antodona@itc.it, serafini@itc.it
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

Matching algorithms automatically discover semantic relations between two autonomously developed conceptual representations of two overlapping domains. Typical examples of such conceptualisations are electronic market catalogues (e.g., unspsc and eCl@ss) and Web directories (e.g., google and yahoo). The objective of this paper is the description of a use case in which the matching algorithm CtxMatch, developed at ITC-IRST and the University of Trento, has been used to re-classify into the Universal Standard Products and Services Classification (unspsc) the catalogue of office equipment and accessories used by a worldwide telecommunication company to classify its suppliers. On the basis of this experience we are envisaging new applications of the algorithm in the area of demand aggregation. We conclude the paper by briefly describing a future application in this area.

Information

Type
Research Article
Copyright
© 2004 Cambridge University Press