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EnglishIn this paper we carry on the work on Onto-Relational Learning by investigating the impact of having disjunctive Datalog with default negation either in the language of hypotheses or in the language for the background theory. The inclusion of nonmonotonic features strengthens the ability of our ILP framework to deal with incomplete knowledge. One such ability can turn out to be useful in application domains, such as the Semantic Web. As a showcase we face the problem of inducing an integrity theory for a relational database whose instance is given and whose schema encompasses an ontology and a set of rules linking the database to the ontology.
Nonmonotonic Onto-Relational Learning / LISI F.A.; ESPOSITO; F. - 5989(2009), pp. 88-95. ((Intervento presentato al convegno 19th International Conference on Inductive Logic Programming, ILP 2009.
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| Titolo: | Nonmonotonic Onto-Relational Learning |
| Autori: | |
| Data di pubblicazione: | 2009 |
| Rivista: | |
| Citazione: | Nonmonotonic Onto-Relational Learning / LISI F.A.; ESPOSITO; F. - 5989(2009), pp. 88-95. ((Intervento presentato al convegno 19th International Conference on Inductive Logic Programming, ILP 2009. |
| Abstract: | In this paper we carry on the work on Onto-Relational Learning by investigating the impact of having disjunctive Datalog with default negation either in the language of hypotheses or in the language for the background theory. The inclusion of nonmonotonic features strengthens the ability of our ILP framework to deal with incomplete knowledge. One such ability can turn out to be useful in application domains, such as the Semantic Web. As a showcase we face the problem of inducing an integrity theory for a relational database whose instance is given and whose schema encompasses an ontology and a set of rules linking the database to the ontology. |
| Handle: | http://hdl.handle.net/11586/14427 |
| ISBN: | 978-3-642-13839-3 |
| Appare nelle tipologie: | 2.1 Contributo in volume (Capitolo o Saggio) |
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