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Probabilistic Knowledge Bases
October 1995 (vol. 7 no. 5)
pp. 691-698

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.

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Index Terms:
Axiomatic probability theory, data mining, incomplete information, knowledge discovery in databases, query optimization and approximation, stratified Datalog.
Citation:
Beat Wüthrich, "Probabilistic Knowledge Bases," IEEE Transactions on Knowledge and Data Engineering, vol. 7, no. 5, pp. 691-698, Oct. 1995, doi:10.1109/69.469827
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