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Ontology Learning and Reasoning - Dealing with Uncertainty and Inconsistency

Veröffentlicht: 2008
Herausgeber: Paulo C. G. Costa, Claudia d'Amato, Nicola Fanizzi, Kathryn B. Laskey, Kenneth J. Laskey, Thomas Lukasiewicz, Matthias Nickles, Mike Pool
Buchtitel: Uncertainty Reasoning for the Semantic Web I
Ausgabe: 5327
Seiten: 366-384
Reihe: Lecture Notes in Computer Science
Verlag: Springer


Ontology learning aims at generating domain ontologies from various kinds of resources by applying natural language processing and machine learning techniques. It is inherent to the ontology learning process that the acquired ontologies represent uncertain and possibly contradicting knowledge. From a logical perspective, the learned ontologies are potentially inconsistent knowledge bases, that as such do not allow for meaningful reasoning. In this paper, we present an approach to generating consistent OWL ontologies from automatically generated or enriched ontology models, which takes into account the uncertainty of the acquired knowledge. We illustrate and evaluate the application of our approach with two experiments in the scenarios of consistent evolution of learned ontologies and enrichment of ontologies with disjointness axioms.

ISBN: 978-3-540-89764-4





Logik, Ontology Learning, Semantic Web