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Enriching Ontologies by Learned Negation

Or How to Teach Ontologies Vegetarianism

  • Conference paper
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Part of the Lecture Notes in Computer Science book series (LNISA,volume 6644)

Abstract

Ontologies form the basis of the semantic web by providing knowledge on concepts, relations and instances. Unfortunately, the manual creation of ontologies is a time intensive and hence expensive task. This leads to the so-called knowledge acquisition bottleneck being a major problem for a more widespread adoption of the semantic web. Ontology learning tries to widen the bottleneck by supporting human knowledge engineers in creating ontologies. For this purpose, knowledge is extracted from existing data sources and is transformed into ontologies. So far, most ontology learning approaches are limited to very basic types of ontologies consisting of concept hierarchies and relations but do not use large amounts of the expressivity ontologies provide.

Keywords

  • Sentiment Analysis
  • Inductive Logic Programming
  • Biomedical Domain
  • Biomedical Text
  • Ontology Learning

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Fleischhacker, D. (2011). Enriching Ontologies by Learned Negation. In: , et al. The Semanic Web: Research and Applications. ESWC 2011. Lecture Notes in Computer Science, vol 6644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21064-8_44

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  • DOI: https://doi.org/10.1007/978-3-642-21064-8_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21063-1

  • Online ISBN: 978-3-642-21064-8

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