Knowledge discovery in molecular databases

Author

Conklin, Darrell ; Fortier, Suzanne ; Glasgow, Janice

Author_Institution

Dept. of Comput. & Inf. Sci., Queen´´s Univ., Kingston, Ont., Canada

Volume

5

Issue

6

fYear

1993

fDate

12/1/1993 12:00:00 AM

Firstpage

985

Lastpage

987

Abstract

An approach to knowledge discovery in complex molecular databases is described. The machine learning paradigm used is structured concept formation, in which object´s described in terms of components and their interrelationships are clustered and organized in a knowledge base. Symbolic images are used to represent classes of structured objects. A discovered molecular knowledge base is successfully used in the interpretation of a high resolution electron density map

Keywords

case-based reasoning; chemistry computing; deductive databases; factographic databases; learning (artificial intelligence); relational databases; visual databases; case-based reasoning; chemical information retrieval; conceptual clustering; description logics; high resolution electron density map; indexing; knowledge base; knowledge discovery; machine learning paradigm; molecular databases; molecular knowledge base; relational models; scene analysis; spatial concepts; spatial reasoning; structured concept formation; symbolic images; Electrons; Image analysis; Image databases; Image reconstruction; Intelligent robots; Knowledge representation; Logic; Machine learning; Proteins; Spatial resolution;

fLanguage

English

Journal_Title

Knowledge and Data Engineering, IEEE Transactions on

Publisher

ieee

ISSN

1041-4347

Type

jour

DOI

10.1109/69.250082

Filename

250082