Ontology Learning and Reasoning -- Dealing with Uncertainty and Inconsistency

  • Authors:

  • Peter Haase;Johanna Völker

  • Affiliations:

  • Institute AIFB, University of Karlsruhe, Germany;Institute AIFB, University of Karlsruhe, Germany

  • Venue:
  • Uncertainty Reasoning for the Semantic Web I
  • Year:
  • 2008

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Abstract

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.