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QuenchML: A semantics-preserving markup language for knowledge representation in quenching

Published online by Cambridge University Press:  15 January 2013

Aparna S. Varde ,
Mohammed Maniruzzaman  and
Richard D. Sisson Jr.
Aparna S. Varde*
Affiliation:
Department of Computer Science, Montclair State University, Montclair, New Jersey, USA
Mohammed Maniruzzaman
Affiliation:
Center for Heat Treating Excellence, Metal Processing Institute, Worcester Polytechnic Institute, Worcester, Massachusetts, USA
Richard D. Sisson Jr.
Affiliation:
Department of Manufacturing and Materials Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts, USA
*
Reprint requests to: Aparna S. Varde, Department of Computer Science, Montclair State University, 1 Normal Avenue, Montclair, NJ 07043, USA. E-mail: [email protected]
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Abstract

Knowledge representation (KR) is an important area in artificial intelligence (AI) and is often related to specific domains. The representation of knowledge in domain-specific contexts makes it desirable to capture semantics as domain experts would. This motivates the development of semantics-preserving standards for KR within the given domain. In addition to the storage and analysis of information using such standards, the effect of globalization today necessitates the publishing of information on the Web. Thus, it is advisable to use formats that make the information easily publishable and accessible while developing KR standards. In this article, we propose such a standard called Quenching Markup Language (QuenchML). This follows the syntax of the eXtensible Markup Language and captures the semantics of the quenching domain within the heat treating of materials. We describe the development of QuenchML, a multidisciplinary effort spanning the realms of AI, database management, and materials science, considering various aspects such as ontology, data modeling, and domain-specific constraints. We also explain the usefulness of QuenchML in semantics-preserving information retrieval and in text mining guided by domain knowledge. Furthermore, we outline the significance of this work in software tools within the field of AI.

Keywords

Domain ExpertiseHeat Treating of MaterialsInformation RetrievalSemantic WebText Mining
Type
Practicum Article
Information
AI EDAM , Volume 27 , Issue 1 , February 2013 , pp. 65 - 82
Copyright
Copyright © Cambridge University Press 2013

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