Towards an Ontology for Data-driven Discovery of New Materials

Cheung, Kwok, Drennan, John and Hunter, Jane (2008). Towards an Ontology for Data-driven Discovery of New Materials. In: Semantic Scientific Knowledge Integration AAAI/SSS Workshop, Stanford University, Palo Alto, CA, (9-14). 26-28 March, 2008.

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Author Cheung, Kwok
Drennan, John
Hunter, Jane
Title of paper Towards an Ontology for Data-driven Discovery of New Materials
Conference name Semantic Scientific Knowledge Integration AAAI/SSS Workshop
Conference location Stanford University, Palo Alto, CA
Conference dates 26-28 March, 2008
Journal name AAAI Spring Symposium - Technical Report
Place of Publication California, USA
Publisher The Association for the Advancement of Artificial Intelligence (AAAI)
Publication Year 2008
Sub-type Fully published paper
Open Access Status File (Author Post-print)
ISBN 9781577353614
Volume SS-08-05
Start page 9
End page 14
Total pages 6
Language eng
Abstract/Summary Materials scientists and nano-technologists are struggling with the challenge of managing the large volumes of multivariate, multidimensional and mixed-media data sets being generated from the experimental, characterisation, testing and post-processing steps associated with their search for new materials. In addition, they need to access large publicly available databases containing: crystallographic structure data; thermodynamic data; phase stability data and ionic conduction data. Materials scientists are demanding data integration tools to enable them to search across these disparate databases and to correlate their experimental data with the public databases, in order to identify new fertile areas for searching. Systematic data integration and analysis tools are required to generate targeted experimental programs that reduce duplication of costly compound preparation, testing and characterisation. This paper presents MatOnto – an extensible ontology, based on the DOLCE upper ontology, that aims to represent structured knowledge about materials, their structure and properties and the processing steps involved in their composition and engineering. The primary aim of MatOnto is to provide a common, extensible model for the exchange, re-use and integration of materials science data and experimentation.
Subjects 280502 Data Storage Representations
291400 Materials Engineering
280000 Information, Computing and Communication Sciences
0899 Other Information and Computing Sciences
Keyword Nanomaterials
Materials ontology
Semantic web
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

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Created: Mon, 16 Jun 2008, 15:12:12 EST by Mr Christopher Davoren on behalf of Faculty Of Engineering, Architecture & Info Tech