Probabilistic knowledge bases

Author

Wüthrich, Beat

Author_Institution

Hong Kong Univ. of Sci. & Technol., Kowloon, Hong Kong

Volume

7

Issue

5

fYear

1995

fDate

10/1/1995 12:00:00 AM

Firstpage

691

Lastpage

698

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;

fLanguage

English

Journal_Title

Knowledge and Data Engineering, IEEE Transactions on

Publisher

ieee

ISSN

1041-4347

Type

jour

DOI

10.1109/69.469827

Filename

469827