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

Aparna Varde, Mohammed Maniruzzaman, Richard D. Sisson

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)65-82
Number of pages18
JournalArtificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM
Volume27
Issue number1
DOIs
StatePublished - 1 Feb 2013

Fingerprint

Markup languages
Knowledge representation
Quenching
Semantics
Artificial intelligence
Management science
Materials science
Information retrieval
XML
Ontology
Data structures

Keywords

  • Heat Treating of Materials
  • Information Retrieval
  • Keywords Domain Expertise
  • Semantic Web
  • Text Mining

Cite this

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QuenchML : A semantics-preserving markup language for knowledge representation in quenching. / Varde, Aparna; Maniruzzaman, Mohammed; Sisson, Richard D.

In: Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM, Vol. 27, No. 1, 01.02.2013, p. 65-82.

Research output: Contribution to journalArticle

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