Abstract
The principle of maximum entropy inductively completes the knowledge given by a knowledge base \(\mathcal R\), and it has been suggested to view learning as an operation being inverse to inductive knowledge completion. While a corresponding learning approach has been developed when \(\mathcal R\) is based on propositional logic, in this paper we describe an extension to a relational setting. It allows to learn relational FO-PCL knowledge bases containing both generic conditionals as well as specific conditionals referring to exceptional individuals from a given probability distribution.
The research reported here was partially supported by the Deutsche Forschungsgemeinschaft (grant BE 1700/7-2).
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Potyka, N., Beierle, C. (2012). An Approach to Learning Relational Probabilistic FO-PCL Knowledge Bases. In: Hüllermeier, E., Link, S., Fober, T., Seeger, B. (eds) Scalable Uncertainty Management. SUM 2012. Lecture Notes in Computer Science(), vol 7520. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33362-0_52
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DOI: https://doi.org/10.1007/978-3-642-33362-0_52
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