Investigating the use of background knowledge for assessing the relevance of statements to an ontology in ontology evolution

Fouad Zablith, Mathieu d’Aquin, Reka Marta Sabou, Enrico Motta

Publication: Chapter in book/Conference proceedingContribution to conference proceedings


The tasks of learning and enriching ontologies with new concepts and relations have attracted a lot of attention in the research community, leading to a number of tools facilitating the process of building and updating ontologies. These tools often discover new elements of information to be included in the considered ontology from external data sources such as text documents or databases, transforming these elements into ontology compatible statements or axioms. While some techniques are used to make sure that statements to be added are compatible with
the ontology (e.g. through conflict detection), such tools generally pay little attention to the relevance of the statement in question. It is either assumed that any statement extracted from a data source is relevant, or that the user will assess whether a statement adds value to the ontology.
In this paper, we investigate the use of background knowledge about the context where statements appear to assess their relevance. We devise a methodology to extract such a context from ontologies available online, to map it to the considered ontology and to visualize this mapping in a way that allows to study the intersection and complementarity of the two sources of knowledge. By applying this methodology on several examples, we identified an initial set of patterns giving strong indications concerning the relevance of a statement, as well as interesting issues to be considered when applying such techniques.
Original languageEnglish
Title of host publicationProceedings of the 3rd International Workshop on Ontology Dynamics (IWOD 2009), Washington DC, USA, October 26, 2009. CEUR Workshop Proceedings, 2009, Vol.519
Editors Mathieu d'Aquin, Grigoris Antoniou
Place of PublicationWashington DC, USA
PublisherCEUR Workshop Proceedings
Pages1 - 14
Publication statusPublished - 2009

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 102
  • 102001 Artificial intelligence
  • 102015 Information systems
  • 102022 Software development

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