Mehmet S. Aktas
Geoffrey C. Fox
David Leake
ABSTRACT
Locating resources of interest in a large resource-intensive environment is a challenging problem. In this paper we present research on addressing this problem through the development of a recommender system to aid in metadata discovery. Our recommender approach uses Conversational Case-Based Reasoning (CCBR), with semantic web markup languages providing a standard form for case representation. We present our initial efforts in designing and developing ontologies for an Earthquake Simulation Grid, to use these to guide case retrieval, discuss how these are exploited in a prototype application, and identify future steps for this approach.
- D. Aha and L. Breslow and H. Munoz-Avila: Conversational Case-Based Reasoning, Applied Intelligence Journal, Volume 14, Pages 9-32, 2001. Google Scholar
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- Choonhan Youn, Marlon Pierce, and Geoffrey Fox: Building Problem Solving Environments with Application Web Service Toolkits, ICCS03 Australia June 2003. Google ScholarDigital Library
- Marlon Pierce, Choonhan Youn, Ozgur Balsoy, Geoffrey Fox, Steve Mock, and Kurt Mueller: Interoperable Web Services for Computational Portals. SC02 2002. Google ScholarDigital Library
- Marlon Pierce, Choonhan Youn, and Geoffrey Fox: Interacting Data Services for Distributed Earthquake Modeling. ACES Workshop at ICCS June 2003 Australia Google ScholarDigital Library
- D. Leake, editor. Case-Based Reasoning: Experiences, Lessons, and Feature Directions. AAAI Press/MIT Press, Menlo Park, CA, 1996. Google ScholarDigital Library
- Gardigen D., Watson I (1998). A web based Case-Based Reasoning System for HVAC Sales Support. Proceedings of British Expert Systems Conference 1998.Google Scholar
- Hayes, C., Cunningham, P., Doyle, M., Distributed CBR using XML. In Proceedings of the KI-98 Workshop on Intelligent Systems and Electronic Commerce, number LSA- 98-03E. University of Kaiserslauten Computer Science Department, 1998.Google Scholar
- W3C Semantic Web Site: http://www.w3.org/2001/swGoogle Scholar
- W3C - Resource Description Framework Site: http://www.w3.org/RDFGoogle Scholar
- Steve Boegarts, David Leake, A Framework For Rapid and Modular Case Based Reasoning System Development: http://www.cs.indiana.edu/sbogaert/ CBR/IUCBRF.pdfGoogle Scholar
- Mehmet S. Aktas, Marlon Pierce, and Geoffrey C. Fox, Designing Ontologies and Distributed Resource Discovery Services for an Earthquake Simulation Grid GGF-11 Global Grid Forum Semantic Grid Applications Workshop Hawaii June 10 2004.Google Scholar
- Link to the SERVOGrid ontologies: http://ripvanwinkle.ucs.indiana.edu:4780/examples- /download/schemaGoogle Scholar
- Link to the SERVO ontology instances: http://ripvanwinkle.ucs.indiana.edu:4780/examples- /download/dataGoogle Scholar
- Dublin Core Metadata Initiative Web Site: http://dublincore.org/documents/dcesGoogle Scholar
- The vCard version 3.0 Specifications: http://www.ietf.org/rfc/rfc2426.txtGoogle Scholar
- Link to the web site of CCBR project presented here: http://complexity.ucs.indiana.edu/ maktas/servo- /project.htmlGoogle Scholar
- Goker, M., Thompson, C.: Personalized conversational case based recommendation Proceedings of the Fifth European Workshop on Case Based Reasoning. Trento, Italy. Google ScholarDigital Library
- Aha D., Breslow L., "Refining Conversational Case Libraries", in Leake D., Plaza E. (eds.) "Case Based Reasoning Research and Development, 2nd International Conference on Case Based Reasoning ICCBR 1997', pp.267-268, Springer Verlag, Berlin 1997. Google ScholarDigital Library
- Leake D., "The Experience Sharing Architecture: A Case Study in Corporate-Wide Case-Based Software Quality Control", in Leake. D., "Case-Based Reasoning: Experiences, Lessons, and Future Directions", pp. 235-268, AAAI Press, Menlo Park, CA, 1996.Google Scholar
- David C. Wilson, David B. Leake, Randall Bramley Case Based Recommender Components for Scientific Problem-Solving Environments Proceedings of the Sixteenth IMACS World Congress, 2000.Google Scholar
- Lorcan Coyle, Conor Hayes, Padraig Cunningham, Representing Cases for CBR in XML, In Proceedings of 7 th UKCBR Workshop, Peterhouse, Cambridge, UK, 2002.Google Scholar
- Robin Burke, The Wasabi Personal Shopper: A Case Based Recommender System, Prooceedings of the 11th Conference on Innovative Applications of Artificial Intelligence. Google ScholarDigital Library
- Goker, M., Thompson, C.: The Adaptive Place Advisor: A Conversational Recommendation System, Proceedings of the 8th German Workshop on Case Based Reasoning. Lammerbuckel, Germany.Google Scholar
- Kautz, H and B. Selman, Creating Models of Real-World Communities with Referral Web, Working notes of the Workshop on Recommender Systems, held in conjunction with AAAI-98, Madison, WI, 1998.Google Scholar
- E.N. Houstis and A. Catlin and J. Rice and V. Verykios and N. Ramakrishnan and C. Houstis, PYTHIA II: A Knowledge/Database System for Managing Performance Data and Recommending Scientific Software, ACM Transactions on Mathematical Software Journal, Volume 26, pp. 227-253, 2000. Google ScholarDigital Library
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