Time Evolving Expert Systems Design and Implementation: The KAFKA Approach

Fabio Sartori, Riccardo Melen

2015

Abstract

Expert Systems design and implementation has been always conceived as a centralized activity, characterized by the relationship between users, domain experts and knowledge engineers. The growing diffusion of sophisticated PDAs and mobile operating systems opens up to new and dynamic application environments and requires to rethink this statement. New frameworks for expert systems design, in particular rule–based systems, should be developed to allow users and domain experts to interact directly, minimizing the role of knowledge engineer and promoting the real–time updating of knowledge bases when needed. This paper present the KAFKA approach to this challenge, based on the implementation of the Knowledge Artifact conceptual model supported by Android OS devices.

References

  1. Angele, J., Fensel, D., Landes, D., and Studer, R. (1998). Developing knowledge-based systems with mike. Automated Software Engineering, 5(4):389-418.
  2. Bonastre, A., Ors, R., and Peris, M. (2001). Distributed expert systems as a new tool in analytical chemistry. TrAC - Trends in Analytical Chemistry, 20(5):263- 271.
  3. Butler, T., Feller, J., Pope, A., Emerson, B., and Murphy, C. (2008). Designing a core it artefact for knowledge management systems using participatory action research in a government and a non-government organisation. The Journal of Strategic Information Systems, 17(4):249-267.
  4. Holsapple, C. W. and Joshi, K. D. (2001).
  5. Kitamura, Y., Kashiwase, M., Fuse, M., and Mizoguchi, R. (2004). Deployment of an ontological framework of functional design knowledge. Advanced Engineering Informatics, 18(2):115-127.
  6. Melen, R., Sartori, F., and Grazioli, L. (2015). Modeling and understanding time-evolving scenarios. In Proceedings of the 19th World Multiconference on Systemics, Cybernetics and Informatics (WMSCI 2015) - Volume I, pages 267-271.
  7. Nalepa, G. and Lige?za, A. (2010). The hekate methodology. hybrid engineering of intelligent systems. International Journal of Applied Mathematics and Computer Science, 20(1):35-53.
  8. Niedderer, K. and Reilly, L. (2010). Research practice in art and design: Experiential knowledge and organised inquiry. Journal of Research Practice, 6(2).
  9. Norman, D. A. (1991). Cognitive artifacts. In Designing interaction, pages 17-38.
  10. Omicini, A., Ricci, A., and Viroli, M. (2008). Artifacts in the a&a meta-model for multi-agent systems. Autonomous agents and multi-agent systems, 17(3):432- 456.
  11. Ruiz-Mezcua, B., Garcia-Crespo, A., Lopez-Cuadrado, J. L., and Gonzalez-Carrasco, I. (2011). An expert system development tool for non ai experts. Expert Systems with Applications, 38(1):597-609.
  12. Rybina, G. V. and Deineko, A. O. (2011). Distributed knowledge acquisition for the automatic construction of integrated expert systems. Scientific and Technical Information Processing, 38(6):428-434.
  13. S. Grimaz (coord.), e. a. (2010). Vademecum STOP. Shoring templates and operating procedures for the support of buildings damaged by earthquakes. Ministry of Interior - Italian Fire Service.
  14. Salazar-Torres, G., Colombo, E., Da Silva, F. C., Noriega, C., and Bandini, S. (2008). Design issues for knowledge artifacts. Knowledge-based systems, 21(8):856- 867.
  15. Sartori, F. and Grazioli, L. (2014). Modeling and understanding time-evolving scenarios. In Metadata and Semantics Research - 8th Research Conference, MTSR 2014, Karlsruhe, Germany, November 27-29, 2014. Proceedings, pages 60-67.
  16. Schmidt, K. and Simone, C. (2000). Mind the gap. Towards a unified view of CSCW. COOP, pages 205-221.
  17. Schreiber, G. (2013). Knowledge acquisition and the web. International Journal of Human Computer Studies, 71(2):206-210.
  18. Schreiber, G., Wielinga, B., de Hoog, R., Akkermans, H., and Van de Velde, W. (1994). Commonkads: A comprehensive methodology for kbs development. IEEE expert, 9(6):28-37.
  19. Surif, J., Ibrahim, N. H., and Mokhtar, M. (2012). Conceptual and procedural knowledge in problem solving. Procedia - Social and Behavioral Sciences, 56:416- 425.
  20. Yan, H., Jiang, Y., Zheng, J., Fu, B., Xiao, S., and Peng, C. (2004). The internet-based knowledge acquisition
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Paper Citation


in Harvard Style

Sartori F. and Melen R. (2015). Time Evolving Expert Systems Design and Implementation: The KAFKA Approach . In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2015) ISBN 978-989-758-158-8, pages 84-95. DOI: 10.5220/0005612900840095


in Bibtex Style

@conference{keod15,
author={Fabio Sartori and Riccardo Melen},
title={Time Evolving Expert Systems Design and Implementation: The KAFKA Approach},
booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2015)},
year={2015},
pages={84-95},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005612900840095},
isbn={978-989-758-158-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2015)
TI - Time Evolving Expert Systems Design and Implementation: The KAFKA Approach
SN - 978-989-758-158-8
AU - Sartori F.
AU - Melen R.
PY - 2015
SP - 84
EP - 95
DO - 10.5220/0005612900840095