TY - JOUR
T1 - QuenchML
T2 - A semantics-preserving markup language for knowledge representation in quenching
AU - Varde, Aparna S.
AU - Maniruzzaman, Mohammed
AU - Sisson, Richard D.
PY - 2013/2
Y1 - 2013/2
N2 - 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.
AB - 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.
KW - Heat Treating of Materials
KW - Information Retrieval
KW - Keywords Domain Expertise
KW - Semantic Web
KW - Text Mining
UR - http://www.scopus.com/inward/record.url?scp=84872453833&partnerID=8YFLogxK
U2 - 10.1017/S0890060412000352
DO - 10.1017/S0890060412000352
M3 - Article
AN - SCOPUS:84872453833
SN - 0890-0604
VL - 27
SP - 65
EP - 82
JO - Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM
JF - Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM
IS - 1
ER -