Title :
Probabilistic knowledge bases
Author_Institution :
Hong Kong Univ. of Sci. & Technol., Kowloon, Hong Kong
fDate :
10/1/1995 12:00:00 AM
Abstract :
We define a new fixpoint semantics for rule based reasoning in the presence of weighted information. The semantics is illustrated on a real world application requiring such reasoning. Optimizations and approximations of the semantics are shown so as to make the semantics amenable to very large scale real world applications. We finally prove that the semantics is probabilistic and reduces to the usual fixpoint semantics of stratified Datalog if all information is certain. We implemented various knowledge discovery systems which automatically generate such probabilistic decision rules. In collaboration with a bank in Hong Kong we use one such system to forecast currency exchange rates
Keywords :
DATALOG; deductive databases; financial data processing; inference mechanisms; knowledge acquisition; knowledge based systems; probability; query languages; query processing; Hong Kong bank; currency exchange rate forecasting; data mining; fixpoint semantics; incomplete information; knowledge discovery; knowledge discovery systems; probabilistic decision rules; probabilistic knowledge bases; query optimization; real world application; rule based reasoning; stratified Datalog; very large scale real world applications; weighted information; Appraisal; Collaboration; Data mining; Databases; Energy management; Exchange rates; Innovation management; Large-scale systems; Logic programming; Query processing;
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
