Title :
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
fDate :
12/1/1993 12:00:00 AM
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;
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
