2003 | OriginalPaper | Buchkapitel
Ideal Refinement of Descriptions in -Log
verfasst von : Francesca A. Lisi, Donato Malerba
Erschienen in: Inductive Logic Programming
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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This paper deals with learning in $\mathcal{AL}$-log, a hybrid language that merges the function-free Horn clause language Datalog and the description logic $\mathcal{ALC}$. Our application context is descriptive data mining. We introduce $\mathcal{O}$-queries, a rule-based form of unary conjunctive queries in $\mathcal{AL}$-log, and a generality order ≽ B for structuring spaces of $\mathcal{O}$-queries. We define a (downward) refinement operator ρ O for ≽ B -ordered spaces of $\mathcal{O}$-queries, prove its ideality and discuss an efficient implementation of it in the context of interest.