C4.5: programs for machine learning
C4.5: programs for machine learning
Creating Domain Specific Metadata for Scientific Data and Knowledge Bases
IEEE Transactions on Knowledge and Data Engineering
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In many fields dependant upon complex observation, the structuring, depiction and treatment of knowledge can be of great complexity. For example in Systematics, the scientific discipline that investigates bio-diversity, the descriptions of specimens are often highly structured (composite objects, taxonomic attributes), noisy (erroneous or unknown data), and polymorphous (variable or imprecise data). In this paper, we present IKBS, an Iterative Knowledge Base System for dealing with such complex phenomena. The originality of this system is to implement the scientific method in biology: experimenting (learning rules from examples) and testing (identifying new individuals, improving the initial model and descriptions). This methodology is applied in the following ways in IKBS: 1 - Knowledge is acquired through a descriptive model that suits the semantic demand of experts. 2 Knowledge is processed with an algorithm derived from C4n.5 i order to take into account structured knowledge introduced in the previous descriptive model of the domain. 3 - Knowledge is refined through eth use of an iterative process to evaluate the robustness of the descriptive model and descriptions. The IKBS system is presented here as a elif science application facilitating the identification of coral specimens of the family Pocilloporidæ.