Titlebook: Inductive Logic Programming; 25th International C Katsumi Inoue,Hayato Ohwada,Akihiro Yamamoto Conference proceedings 2016 Springer Interna

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CARAF: Complex Aggregates within Random Forests,. CARAF, for Complex Aggregates within RAndom Forests, has two goals. Firstly, it aims at avoiding exhaustive exploration of the large feature space induced by the use of complex aggregates. Its second purpose is to reduce the overfitting introduced by the expressivity of complex aggregates in the c

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Distributed Parameter Learning for Probabilistic Ontologies,E, for “Em over bDds for description loGics paramEter learning”, is an algorithm for learning the parameters of probabilistic ontologies from data. However, the computational cost of this algorithm is significant since it may take hours to complete an execution. In this paper we present ., a distrib

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Statistical Relational Learning with Soft Quantifiers, using soft quantifiers, such as “most” and “a few”. In this paper, we define the syntax and semantics of PSL., a new SRL framework that supports reasoning with soft quantifiers, and present its most probable explanation (MPE) inference algorithm. To the best of our knowledge, PSL. is the first SRL

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Ontology Learning from Interpretations in Lightweight Description Logics,ues for (semi-)automating this task is therefore practically vital — yet, hindered by the lack of robust theoretical foundations. In this paper, we study the problem of learning Description Logic TBoxes from interpretations, which naturally translates to the task of ontology learning from data. In t