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Metadata for Search Engines: What can be learned from e-Sciences?

Metadata for Search Engines: What can be learned from e-Sciences?

Magali Roux
Copyright: © 2012 |Pages: 31
ISBN13: 9781466603301|ISBN10: 1466603305|EISBN13: 9781466603318
DOI: 10.4018/978-1-4666-0330-1.ch003
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MLA

Roux, Magali. "Metadata for Search Engines: What can be learned from e-Sciences?." Next Generation Search Engines: Advanced Models for Information Retrieval, edited by Christophe Jouis, et al., IGI Global, 2012, pp. 47-77. https://doi.org/10.4018/978-1-4666-0330-1.ch003

APA

Roux, M. (2012). Metadata for Search Engines: What can be learned from e-Sciences?. In C. Jouis, I. Biskri, J. Ganascia, & M. Roux (Eds.), Next Generation Search Engines: Advanced Models for Information Retrieval (pp. 47-77). IGI Global. https://doi.org/10.4018/978-1-4666-0330-1.ch003

Chicago

Roux, Magali. "Metadata for Search Engines: What can be learned from e-Sciences?." In Next Generation Search Engines: Advanced Models for Information Retrieval, edited by Christophe Jouis, et al., 47-77. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-0330-1.ch003

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Abstract

E-sciences are data-intensive sciences that make a large use of the Web to share, collect, and process data. In this context, primary scientific data is becoming a new challenging issue as data must be extensively described (1) to account for empiric conditions and results that allow interpretation and/or analyses and (2) to be understandable by computers used for data storage and information retrieval. With this respect, metadata is a focal point whatever it is considered from the point of view of the user to visualize and exploit data as well as this of the search tools to find and retrieve information. Numerous disciplines are concerned with the issues of describing complex observations and addressing pertinent knowledge. In this paper, similarities and differences in data description and exploration strategies among disciplines in e-sciences are examined.

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