Sciweavers

Share
SEMWEB
2005
Springer

Ontology Learning and Reasoning - Dealing with Uncertainty and Inconsistency

10 years 9 months ago
Ontology Learning and Reasoning - Dealing with Uncertainty and Inconsistency
Ontology Learning from text aims at generating domain ontologies from textual resources by applying natural language processing and machine learning techniques. It is inherent in 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 thus do not allow meaningful reasoning directly. In this paper we present an approach to generate consistent OWL ontologies from learned ontology models by taking the uncertainty of the knowledge into account. We further present evaluation results from experiments with ontologies learned from a Digital Library.
Peter Haase, Johanna Völker
Added 28 Jun 2010
Updated 28 Jun 2010
Type Conference
Year 2005
Where SEMWEB
Authors Peter Haase, Johanna Völker
Comments (0)
books