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• W1818677499 abstract "Rules complement and extend ontologies on the Semantic Web. We refer to these rules as onto-relational since they combine DL-based ontology languages and Knowledge Representation formalisms supporting the relational data model within the tradition of Logic Programming and Deductive Databases. Rule authoring is a very demanding Knowledge Engineering task which can be automated though partially by applying Machine Learning algorithms. In this chapter we show how Inductive Logic Programming (ILP), born at the intersection of Machine Learning and Logic Programming and considered as a major approach to Relational Learning, can be adapted to Onto-Relational Learning. For the sake of illustration, we provide details of a specific Onto-Relational Learning solution to the problem of learning rule-based definitions of DL concepts and roles with ILP." @default.
• W1818677499 created "2016-06-24" @default.
• W1818677499 creator A5079129060 @default.
• W1818677499 date "2012-10-10" @default.
• W1818677499 modified "2023-09-27" @default.
• W1818677499 title "Learning Onto-Relational Rules with Inductive Logic Programming" @default.
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• W1818677499 hasPublicationYear "2012" @default.
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