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

Authors:
K. Selçuk Candan;Jong Wook Kim;Huan Liu;Reshma Suvarna
Affiliations:
Department of Computer Science and Engineering, Arizona State University, 82857, Tempe, AZ, USA;Department of Computer Science and Engineering, Arizona State University, 82857, Tempe, AZ, USA;Department of Computer Science and Engineering, Arizona State University, 82857, Tempe, AZ, USA;Department of Computer Science and Engineering, Arizona State University, 82857, Tempe, AZ, USA
Venue:
Knowledge and Information Systems
Year:
2006

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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.