Abstract
This chapter describes how to identify and collect human knowledge and transform it into machine readable and actionable knowledge. We will focus on the knowledge acquisition for distributed case-based reasoning systems. Case-based reasoning (CBR) is a well-known methodology for implementing knowledge-intensive decision support systems (Aamodt, Plaza, Artif Intell Commun, 7(1):39–59, 1994) [1] and has been applied in a broad range of applications. It captures experiences in the form of problem and solution pairs, which are recalled when similar problems reoccur. In order to create a CBR system the initial knowledge has to be identified and captured. In this chapter, we will summarise the knowledge acquisition method presented by Bach, Knowledge acquisition for case-based reasoning systems. Ph.D. thesis, University of Hildesheim, München (2012) [2] and give an running example within the travel medicine domain utilising the open source tool for developing CBR systems, myCBR.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
In the remaining parts of this work we will differentiate between snippet descriptions and snippet. Snippet descriptions are the conceptual representation of knowledge and are equivalent to a case representation. Snippets on the other hand are instances of snippet descriptions and therewith equivalent to cases.
- 4.
- 5.
References
Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. Artif. Intell. Commun. 7(1), 39–59 (1994)
Bach, K.: Knowledge acquisition for case-based reasoning systems. Ph.D. thesis, University of Hildesheim, München (2012). ISBN 978-3-8439-1357
Althoff, K.D., Reichle, M., Bach, K., Hanft, A., Newo, R.: Agent based maintenance for modularised case bases in collaborative multi-expert systems. In: Proceedings of AI2007, 12th UK Workshop on Case-Based Reasoning, pp. 7–18 (2007)
Reichle, M., Bach, K., Althoff, K.D.: Knowledge Engineering within the Application Independent Architecture SEASALT. In: Baumeister, J., Nalepa, G.J. (eds.) International Journal of Knowledge Engineering and Data Mining, pp. 202–215. Inderscience Publishers (2011)
Parnas, D.L.: On the criteria to be used in decomposing systems into modules. Commun. ACM 15, 1053–1058 (1972)
Tautz, C.: Customizing software engineering experience management systems to organizational needs. Ph.D. thesis, Universität Kaiserslautern (2000)
Bergmann, R., Althoff, K.D., Breen, S., Göker, M.H., Manago, M., Traphöner, R., Wess, S.: Selected applications of the structural case-based reasoning approach. Developing Industrial Case-Based Reasoning Applications: The INRECA-Methodology, LNCS, vol. 1612, pp. 35–70. Springer (2003)
Davenport, T.H., Prusak, L.: Working Knowledge: How Organizations Manage What they Know. Harvard Business School Press (2000)
Bach, K., Reuss, P., Althoff, K.D.: Case-based menu creation as an example of individualized experience management. In: Maier, R., Kohlegger, M. (eds.) Professional Knowledge Management. Conference on Professional Knowledge Management (WM-2011), From Knowledge to Action, pp. 194–203. LNI 182, Köllen Druck & Verlag GmbH, Bonn (2011)
Ihle, N., Newo, R., Hanft, A., Bach, K., Reichle, M.: CookIIS - A Case-Based Recipe Advisor. In: Delany, S.J. (ed.) Workshop Proceedings of the 8th International Conference on Case-Based Reasoning, pp. 269–278. Seattle, WA, USA (2009)
Redmond, M.: Distributed Cases for Case-Based Reasoning: Facilitating use of multiple cases. In: AAAI, pp. 304–309 (1990)
Bergmann, R.: Experience Management: Foundations, Development Methodology, and Internet-Based Applications. Lecture Notes in Computer Science, vol. 2432. Springer (2002)
Marter, S.: Case-Based Coordination Agents - Knowledge Modularization and Knowledge Composition (Fallbasierte Koordinationsagenten – Wissensmodularisierung und Wissenskomposition für dezentrale, heterogene Fallbasen). Master’s thesis, Institute of Computer Science, University of Hildesheim (2011)
Bach, K., Althoff, K.D., Newo, R., Stahl, A.: A case-based reasoning approach for providing machine diagnosis from service reports. In: Ram, A., Wiratunga, N. (eds.) Proceedings of the 19th Intl. Conference on Case-Based Reasoning (ICCBR-2011), London, UK, LNCS, vol. 6880, pp. 363–377. Springer, Heidelberg (2011)
Stahl, A., Roth-Berghofer, T.R.: Rapid prototyping of cbr applications with the open source tool mycbr. In: ECCBR ’08: Proceedings of the 9th European conference on Advances in Case-Based Reasoning, pp. 615–629. Springer, Berlin, Heidelberg (2008)
Reuss, P.: Concept and Implementation of Knowledge Line Retrieval Strategies for Modularized, Homogeneous Topic Agents within a Multi-Agent-System (Konzept und Implementierung einer Knowledge line – Retrievalstrategien für modularisierte, homogene Topicagenten innerhalb eines Multi-Agenten-Systems). Hildesheim: Stiftung Universität Hildesheim, Institut für Informatik, Bereich Intelligente Informationssysteme, Master thesis (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Bach, K. (2018). Knowledge Engineering for Distributed Case-Based Reasoning Systems. In: Nalepa, G., Baumeister, J. (eds) Synergies Between Knowledge Engineering and Software Engineering. Advances in Intelligent Systems and Computing, vol 626. Springer, Cham. https://doi.org/10.1007/978-3-319-64161-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-64161-4_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-64160-7
Online ISBN: 978-3-319-64161-4
eBook Packages: EngineeringEngineering (R0)