C4.5: programs for machine learning
C4.5: programs for machine learning
Troubleshooting CFM 56-3 Engines for the Boeing 737 - Using CBR and Data-Mining
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
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Systematics, the scientific discipline that deals with listing, describing, naming, classifying and identifying living organisms is a central point in environmental sciences. Expertise is becoming rare and for future biodiversity studies relying on species identification, environmental technicians will only be left with monographic descriptions and collections in museums. With the emergence of knowledge management on the Internet, it is possible to enhance the use of systematician expertise, by providing them with collaborative tools to widely manage, share and transmit their knowledge. Knowledge engineering in Systematics means to revise descriptions of specimens and to bring them alive on the web. We have designed an Iterative Knowledge Base System ( $\mathcal{IKBS}$) for achieving these goals. It applies the scientific method in biology (conjecture and test) with a natural process of knowledge management. The product of such a tool is a collaborative knowledge base of a domain, that can evolve (by updating the knowledge) and be connected to distributed databases (bibliographic, photographic, geographic, taxonomic, etc.) that will yield information on species after the identification process of a new specimen.This paper presents an overview of the methodology, the methods (identification tree and case-based reasoning) and the validation process used to build knowledge bases in Systematics. An application on corals of the Mascarene Archipelago is given as a case study.