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The Semantic Gap of Formalized Meaning

  • Sebastian Hellmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6089)

Abstract

Recent work in Ontology learning and Text mining has mainly focused on engineering methods to solve practical problem. In this thesis, we investigate methods that can substantially improve a wide range of existing approaches by minimizing the underlying problem: The Semantic Gap between formalized meaning and human cognition. We deploy OWL as a Meaning Representation Language and create a unified model, which combines existing NLP methods with Linguistic knowledge and aggregates disambiguated background knowledge from the Web of Data. The presented methodology here allows to study and evaluate the capabilities of such aggregated knowledge to improve the efficiency of methods in NLP and Ontology learning.

Keywords

Word Sense Disambiguation Linguistic Knowledge Relation Extraction Meaning Representation Formalize Meaning 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Sebastian Hellmann
    • 1
  1. 1.AKSW Research GroupUniversität LeipzigGermany

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