Managing Complex Knowledge in Natural Sciences

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 Pocilloporidae.

[1]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[2]  John Mingers,et al.  Expert Systems—Rule Induction with Statistical Data , 1987 .

[3]  J. Veron,et al.  Scleractinia of Eastern Australia. , 1976 .

[4]  Jim Diederich,et al.  Creating Domain Specific Metadata for Scientific and Knowledge Bases , 1991, IEEE Trans. Knowl. Data Eng..

[5]  K. Popper,et al.  La logique de la découverte scientifique , 1973 .

[6]  M. Manago,et al.  Induction and Reasoning from Cases , 1993 .

[7]  F. A. Bisby,et al.  Databases in Systematics , 1984 .

[8]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[9]  T. A. Paine,et al.  Delta user's guide: a general system for processing taxonomic descriptions. , 1993 .

[10]  Noël Conruyt,et al.  On the representation of observational data used for classification and identification of natural objects , 1994 .

[11]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[12]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[13]  T. A. Paine,et al.  User's guide to the Delta system: a general system for processing taxonomic descriptions , 1993 .

[14]  R. J. Pankhurst,et al.  Practical Taxonomic Computing , 1991 .

[15]  Michel MANAGO,et al.  Using Information Technology to Solve Real World Problems , 1991, Contemporary Knowledge Engineering and Cognition.

[16]  Michel Manago,et al.  VI INDUCTION AND REASONING FROM CASES , 1999 .