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Discovering mappings in hierarchical data from multiple sources using the inherent structure

Discovering mappings in hierarchical data from multiple sources using the inherent structure Unprecedented amounts of media data are publicly accessible. However, it is increasingly difficult to integrate relevant media from multiple and diverse sources for effective applications. The functioning of a multimodal integration system requires metadata, such as ontologies, that describe media resources and media components. Such metadata are generally application-dependent and this can cause difficulties when media needs to be shared across application domains. There is a need for a mechanism that can relate the common and uncommon terms and media components. In this paper, we develop an algorithm to mine and automatically discover mappings in hierarchical media data, metadata, and ontologies, using the structural information inherent in these types of data. We evaluate the performance of this algorithm for various parameters using both synthetic and real-world data collections and show that the structure-based mining of relationships provides high degrees of precision. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Knowledge and Information Systems Springer Journals

Discovering mappings in hierarchical data from multiple sources using the inherent structure

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References (62)

Publisher
Springer Journals
Copyright
Copyright © 2006 by Springer-Verlag
Subject
Computer Science; Business Information Systems; Information Systems and Communication Service
ISSN
0219-1377
eISSN
0219-3116
DOI
10.1007/s10115-005-0230-9
Publisher site
See Article on Publisher Site

Abstract

Unprecedented amounts of media data are publicly accessible. However, it is increasingly difficult to integrate relevant media from multiple and diverse sources for effective applications. The functioning of a multimodal integration system requires metadata, such as ontologies, that describe media resources and media components. Such metadata are generally application-dependent and this can cause difficulties when media needs to be shared across application domains. There is a need for a mechanism that can relate the common and uncommon terms and media components. In this paper, we develop an algorithm to mine and automatically discover mappings in hierarchical media data, metadata, and ontologies, using the structural information inherent in these types of data. We evaluate the performance of this algorithm for various parameters using both synthetic and real-world data collections and show that the structure-based mining of relationships provides high degrees of precision.

Journal

Knowledge and Information SystemsSpringer Journals

Published: Jan 30, 2006

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