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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: vardea@montclair.edu
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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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References

REFERENCES

Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases, ACM SIGMOD, pp. 207–216.Google Scholar
Altova. (2010). XML Spy. Accessed at http://www.altova.com/xml-editor/Google Scholar
Begley, E. (2003). MatML Version 3.0 Schema (NIST Technology Report 6939). MD: National Institute of Standards and Technology.Google Scholar
Boag, S., Fernandez, M., Florescu, D., Robie, J., & Simeon, J. (2003). XQuery 1.0: an XML query language. World Wide Web Consortium. Accessed at http://www.w3.org/TR/xquery/Google Scholar
Bouvier, D.J. (1995). Versions and standards of HTML. ACM SIGAPP Applied Computing Review 3(2), 9–15.CrossRefGoogle Scholar
Carlisle, D., Ion, P., Miner, R., & Poppelier, N. (2001). Mathematical Markup Language (MathML). World Wide Web Consortium. Accessed at http://www.w3.org/TR/REC-MathML/Google Scholar
Chen, P.P. (1976). The entity relationship model—toward a unified view of data. ACM Transactions on Database Systems 1(1), 9–36.CrossRefGoogle Scholar
Chiu, I., & Shu, L.H. (2007). Biomimetic design through natural language analysis to facilitate cross-domain information retrieval. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21(1), 45–59.CrossRefGoogle Scholar
Clark, J. (1999). XSL transformations (XSLT) version 1.0. World Wide Web Consortium, W3C draft.Google Scholar
Clark, J., & DeRose, S. (1999). XML Path Language (XPath) version 1.0. World Wide Web Consortium. Accessed at http://www.w3.org/TR/xpath/Google Scholar
Component Source. (1996). Stylus Studio. Accessed at http://www.componentsource.com/products/stylus-studioGoogle Scholar
Fahrenholz, S. (2006). Materials properties thesaurus development. Paper presented at the ASM International Aeromat Conference, Seattle, WA.Google Scholar
Felfering, A., Friedrich, G., Jananch, D., Stumptner, M., & Zanker, M. (2003). Configuration knowledge representations for Semantic Web applications. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 17(1), 31–50.CrossRefGoogle Scholar
Flynn, P. (2002). The XML FAQ. Accessed at http://xml.silmaril.ie/Google Scholar
Guo, J., Araki, K., Tanaka, K., Sato, J., Suzuki, M., Takada, A., Suzuki, T., Nakashima, Y., & Yoshihara, H. (2003). The MML (Medical Markup Language) version 2.3—XML-based standard for medical data exchange/storage. Journal of Medical Systems 27(4), 357–366.CrossRefGoogle ScholarPubMed
Han, J., & Kamber, M. (2006). Data Mining: Concepts and Techniques (2nd ed.). San Francisco, CA: Morgan Kaufmann.Google Scholar
Jackson, P. (1999). Expert Systems (3rd ed.). Boston: Addison–Wesley.Google Scholar
Klein, D., & Manning, C. (2003). Fast exact inference with a factored model for natural language parsing. Advances in Neural Information Processing Systems 15, 3–10.Google Scholar
Li, Z., & Ramani, K. (2007). Ontology-based design information extraction and retrieval. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21(2), 137–154.CrossRefGoogle Scholar
Marcus, M.P., Santorini, B., & Marcinkiewicz, M.A. (1993). Building a large annotated corpus of English: the Penn treebank. Computational Linguistics 19(2), 313–330.Google Scholar
Miller, G.A., Beckwith, R., Fellbaum, C.D., Gross, D., & Miller, K. (1990). WordNet: an online lexical database. International Journal of Lexicography 3(4), 235–244.CrossRefGoogle Scholar
Mills, A. (1995). Heat and Mass Transfer. New York: Richard Irwin.Google Scholar
Mitchell, T. (1997). Machine Learning. New York: McGraw–HillGoogle Scholar
Murray-Rust, P. (1997). Chemical Markup Language: a simple introduction to structured documents. World Wide Web Journal 2(4), 135–147.Google Scholar
Pellack, L. (2002). Introduction to materials science. Issues in Science and Technology Librarianship Spring. Accessed at http://www.istl.org/02-spring/internet.htmlGoogle Scholar
Rilloff, E. (1996). An empirical study of automated dictionary construction for information extraction in three domains. Artificial Intelligence 85, 101–134.CrossRefGoogle Scholar
Russell, S., & Norvig, P. (2009). Artificial Intelligence: A Modern Approach, 3rd ed.Englewood Cliffs, NJ: Prentice Hall.Google Scholar
Symbol Click. (2001). XML Marker. Accessed at http://symbolclick.com/Google Scholar
Thomere, J., Barker, K., Chaudhri, V., Clark, P., Eriksen, M., Mishra, S., Porter, B., & Rodriguez, A. (2002). A web-based ontology browsing and editing system. Proc. AAAI 1, pp. 927–934.Google Scholar
Totten, G., Bates, C., & Clinton, N. (1993). Handbook of quench technology and quenchants. Materials Park, OH: ASM International.Google Scholar
Varde, A., Aker, M., & Feldman, A. (2009). Automated Rule Extraction Over a Scientific Text Data Warehouse Using a Domain-Specific Markup Language (Technical Report F1209). Montclair, NJ: Montclair State University, Department of Computer Science.Google Scholar
Varde, A., Begley, E., & Fahrenholz, S. (2006). MatML: XML for information exchange with materials property data. Proc. 4th Int. Workshop on Data Mining Standards, Services and Protocols, pp. 47–54.CrossRefGoogle Scholar
Varde, A., Rundensteiner, E., & Fahrenholz, S. (2010). XML-based markup languages for specific domains. Web Based Support Systems, pp. 215–238.CrossRefGoogle Scholar
Varde, A., Rundensteiner, E., Mani, M., Maniruzzaman, M., & Sisson, R. Jr. (2004). Augmenting MatML with heat treating semantics. Proc. ASM Int. Materials Solutions Conf. MatSol, Symp. Developments in Web-Based Material Property Databases.Google Scholar
Varde, A., Rundensteiner, E., Maniruzzaman, M., & Sisson, R. Jr. (2003). The QuenchMiner expert system for quenching and distortion control. Proc. ASM International Heat Treating Society Conference, HTS.Google Scholar
Varde, A., Suchanek, F., Nayak, R., & Senellart, P. (2009). Knowledge discovery over the deep web, semantic web and XML. Proc. DASFAA, pp. 784–788.CrossRefGoogle Scholar
Winston, P. H. (1992). Artificial Intelligence. New York: Pearson Education.Google Scholar
World Wide Web Consortium. (2004 a). W3C OWL. Accessed at http://www.w3.org/TR/owl-guide/Google Scholar
World Wide Web Consortium. (2004 b). W3C RDF. Accessed at http://www.w3.org/TR/rdf-primer/Google Scholar
World Wide Web Consortium. (2004 c). W3C XML Schema. Accessed at http://www.w3.org/TR/xmlschema-0Google Scholar
Witherell, P., Krishnamurty, S., Grosse, I.R., & Wileden, J.C. (2009). Improved knowledge management through first-order logic in engineering design ontologies. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 24(2), 245–257.CrossRefGoogle Scholar
Witten, I., and Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques (2nd ed.). San Francisco, CA: Morgan Kaufmann.Google Scholar
Yokota, K., Kunishima, T., & Liu, B. (2001). Semantic extensions of XML for advanced applications. IEEE Computer Science Communications 23(6), 49–57.Google Scholar