Managing Complex Knowledge in Natural
Sciences

Nol Conruyt, David Grosser

IREMIA, Institut de REcherche en Mathmatiques et Informatique Appliques
University of La Runion
15, av. Ren Cassin  97715 Saint-Denis, Messag. Cedex 9, France
{Conruyt, Grosser}@univ-reunion.fr



Abstract. 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 C4.5 in order to take into account structured
knowledge introduced in the previous descriptive model of the domain. 3 -
Knowledge is refined through the use of an iterative process to evaluate the
robustness of the descriptive model and descriptions. The IKBS system is
presented here as a life science application facilitating the identification of
coral specimens of the family Pocilloporid.
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