Abstract
We prove that any concept in any description logic that extends \(\mathcal{ALC}\) with some features amongst I (inverse), Q k (quantified number restrictions with numbers bounded by a constant k), Self (local reflexivity of a role) can be learnt if the training information system is good enough. That is, there exists a learning algorithm such that, for every concept C of those logics, there exists a training information system consistent with C such that applying the learning algorithm to the system results in a concept equivalent to C.
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Divroodi, A.R., Ha, QT., Nguyen, L.A., Nguyen, H.S. (2012). On C-Learnability in Description Logics. In: Nguyen, NT., Hoang, K., Jȩdrzejowicz, P. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2012. Lecture Notes in Computer Science(), vol 7653. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34630-9_24
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DOI: https://doi.org/10.1007/978-3-642-34630-9_24
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