Research Papers

A Representation-Based Methodology for Developing High-Value Knowledge Engineering Software Products: Theory, Application, and Implementation

[+] Author and Article Information
S. Desa

e-mail: sdesa@soe.ucsc.edu

T. Munger

e-mail: tmunger@soe.ucsc.edu
Technology and Information Management,
Baskin School of Engineering,
University of California,
Santa Cruz, CA 95064

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received May 14, 2013; final manuscript received June 5, 2013; published online September 12, 2013. Editor: Bahram Ravani.

J. Comput. Inf. Sci. Eng 13(4), 041006 (Sep 12, 2013) (19 pages) Paper No: JCISE-13-1092; doi: 10.1115/1.4024914 History: Received May 14, 2013; Revised June 05, 2013

Most enterprises, technology and otherwise, are routinely collecting massive amounts of unstructured information from customer interactions and then attempting to extract knowledge from this information in order to improve core activities such as product development, customer support, and marketing. The knowledge engineering processes for extracting knowledge from this information are often largely manual and extremely inefficient in both cost and time. Therefore, software automation of these manual activities through the creation of highly user-centric Knowledge Engineering Software Products (KESPs) is critical to enabling the rapid and efficient extraction of high-quality knowledge. The primary intent of this paper is to provide a comprehensive theory, including its application and implementation, for developing high-value Knowledge Engineering Software Products. To this end, we have created a representation-based approach to the design and development of KESPs. The theoretical framework of our representational-based approach is the integrated metarepresentational model (IMRM) which provides a natural sequence of representations for guiding the development of complex artifacts such as KESPs. The application of the IMRM to the development of high-value KESPs resulted in the integrated representation-based process methodology (IRPM) which combines, in a rational and structured manner, methods and tools from the technical domains of Knowledge Engineering, Product Design, and Software Engineering. Each domain contributes a distinct set of methods to the IRPM. The knowledge engineering domain provides tools—such as the CommonKADS Agent/Task model—for modeling current work processes that the KESP will automate. The product design domain provides formal tools—such as the House of Quality, Function Structure, Morphological Matrix, and Utility Function—for explicitly defining the user needs for the KESP, and for exploring different design concepts in order to ensure the KESP is high-quality and low-cost. The software engineering domain provides tools—such as Unified Modeling Language (UML) Use Case, Component, and Class diagrams—in order to ensure that a reliable and easy to use KESP is delivered on time and within budget. We have demonstrated the feasibility of the IRPM by implementing it within the context of a real knowledge engineering problem involving the extraction of problem-solution pairs from customer service requests in order to create “smart” products and services. The developed KESP, called the “Service Request Portal” (SRP), used search and content filters to achieve a 30% productivity improvement over the previously manual work process.

Copyright © 2013 by ASME
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Spangler, S., and Kreulen, J., 2008, Mining the Talk: Unlocking the Business Value in Unstructured Information, IBM Press, Upper Saddle River, NJ.
Witten, I., and Frank, E., 2005, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, San Francisco, CA.
Metzinger, T., 2003, Being No-One: The Self-Model Theory of Subjectivity, MIT Press, Cambridge, MA.
Revonsuo, A., 2009, Inner Presence: Consciousness as a Biological Phenomenon, MIT Press, Cambridge, MA.
Winston, P., 1992, Artificial Intelligence, Addison Wesley, Massachusetts.
Brachman, R., and Levesque, H., 2004, Knowledge Representation and Reasoning, Morgan Kaufmann, Amsterdam, The Netherlands.
Schreiber, G., and Wielinga, B., 1994, “Commonkads: A Comprehensive Methodology for KBS Development,” IEEE Expert Syst., 9, pp. 28–37. [CrossRef]
Croft, B., Metzler, D., and Stroham, T., 2009, Search Engines: Information Retrieval in Practice, Addison Wesley, Boston, MA.
Angele, J., Fensel, D., Landes, D., Neubert, S., and Studer, R., 1992, “Model-Based and Incremental Knowledge Engineering: The Mike Approach,” In Extended Papers From the IFIP TC12 Workshop on Artificial Intelligence From the Information Processing Perspective: Knowledge Oriented Software Design (AIFIPP '92), pp. 139–168.
Hauser, J., and Clausing, D., 1988, “The House of Quality,” Harv. Bus. Rev., May–June, pp. 63–73.
Pahl, G., and Beitz, W., 1996, Engineering Design: A Systematic Approach, Springer-Verlag, London.
Hubka, V., Andreasen, N., and Eder, E., 1988, Practical Studies in Systematic Design, Butterworth, London.
Otto, K., and Wood, K., 2000, Product Design: Techniques in Reverse Engineering and Product Design, Prentice–Hall, Englewood Cliffs, NJ.
Pugh, S., 1991, Total Design, Addison Wesley, England.
Schach, S., 2008, Object Oriented Software Engineering, Prentice-Hall, Englewood Cliffs, NJ.
McConnell, S., 1996, Rapid Development: Taming Wild Software Schedules, Microsoft Press, Washington.
Jackson, M., 1983, System Development, Prentice-Hall, Englewood Cliffs, NJ.
Jackson, M., 2001, Problem Frames: Analyzing and Structuring Software Development Problems, Addison Wesley, England.
Boehm, B., 2003, “Value-Based Software Engineering,” ACM SIGSOFT Softw. Eng. Notes, 28(2), p. 4. [CrossRef]
Poole, D., and Mackworth, A., 2010, Artificial Intelligence: Foundations of Computational Agents, Cambridge University Press, Cambridge.
Friedenberg, J., and Silverman, G., 2005, Cognitive Science: An Introduction to the Study of the Mind, Sage, Los Angeles.
Schmitz, J., and Desa, S.1993, “The Development of Virtual Concurrent Engineering and Its Application to Design for Producibility,” Concurrent Eng.1(3), pp. 159–169. [CrossRef]
Schmitz, J. M., and Desa, S., 1994, “Development and Implementation of a Design for Producibility Method for Precision Planar Stamped Products,” ASME J. Mech. Des.,116(2), 349–356. [CrossRef]
Munger, T., 2012, “A Representation-Based Methodology for Developing High-Value Knowledge Engineering Systems: Theory and Applications,” Master’s thesis, University of Califonia, Santa Cruz.
Ulrich, K., and Eppinger, S., 1995, Product Design and Development, Prentice–Hall, Englewood Cliffs, NJ.


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Fig. 1

The integrated metarepresentational model

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Fig. 2

The integrated representation-based process methodology

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Fig. 3

GUI for the Service Request Portal

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Fig. 4

Software architecture for the Service Request Portal

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Fig. 14

Technical metric 1: average number of pages read to assess relevance of a service request

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Fig. 15

Technical metric 2: average number of pages read to assess relevance of a service request

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Fig. 16

Technical metric 3: average time to extract problem-solution pair from a service request



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