Abstract
In this paper we face the coverage problem in the context of learning in the hybrid language \(\mathcal{AL}\)-log. Here candidate hypotheses are represented as Datalog clauses with variables constrained by assertions in the description logic \(\mathcal{ALC}\). Regardless of the scope of induction we define coverage relations for \(\mathcal{AL}\)-log in the two logical settings of learning from implications and learning from interpretations. Also, with reference to the ILP system \(\mathcal{AL}\)-QuIn, we discuss our solutions to the algorithmic and implementation issues raised by the coverage test for the setting of characteristic induction from interpretations in \(\mathcal{AL}\)-log.
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Lisi, F.A., Esposito, F. (2004). Efficient Evaluation of Candidate Hypotheses in \(\mathcal{AL}\)-log. In: Camacho, R., King, R., Srinivasan, A. (eds) Inductive Logic Programming. ILP 2004. Lecture Notes in Computer Science(), vol 3194. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30109-7_18
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DOI: https://doi.org/10.1007/978-3-540-30109-7_18
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