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Unsupervised Ontology Acquisition from Plain Texts: The OntoGain System

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Natural Language Processing and Information Systems (NLDB 2010)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6177))

Abstract

We propose OntoGain, a system for unsupervised ontology acquisition from unstructured text which relies on multi-word term extraction. For the acquisition of taxonomic relations, we exploit inherent multi-word terms’ lexical information in a comparative implementation of agglomerative hierarchical clustering and formal concept analysis methods. For the detection of non-taxonomic relations, we comparatively investigate in OntoGain an association rules based algorithm and a probabilistic algorithm. The OntoGain system allows for transformation of the derived ontology into standard OWL statements. OntoGain results are compared to both hand-crafted ontologies, as well as to a state-of-the art system, in two different domains: the medical and computer science domains.

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Author information

Authors and Affiliations

  1. Intelligent Systems Laboratory, Electronic and Computer Engineering Dept., Technical University of Crete (TUC), Chania, Crete, Greece

    Euthymios Drymonas & Euripides G. M. Petrakis

  2. Tilburg centre for Creative Computing (TiCC), University of Tilburg, The Netherlands

    Kalliopi Zervanou

Authors
  1. Euthymios Drymonas
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  2. Kalliopi Zervanou
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  3. Euripides G. M. Petrakis
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Editor information

Editors and Affiliations

  1. School of Engineering, Cardiff University, UK

    Christina J. Hopfe  & Haijiang Li  & 

  2. Informatics Research Institute, University of Salford, M5 4WT, Greater Manchester, UK

    Yacine Rezgui

  3. Centre National des Arts et Métiers,  

    Elisabeth Métais

  4. School of Computer Science, Cardiff University, UK

    Alun Preece

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Drymonas, E., Zervanou, K., Petrakis, E.G.M. (2010). Unsupervised Ontology Acquisition from Plain Texts: The OntoGain System. In: Hopfe, C.J., Rezgui, Y., Métais, E., Preece, A., Li, H. (eds) Natural Language Processing and Information Systems. NLDB 2010. Lecture Notes in Computer Science, vol 6177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13881-2_29

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  • DOI: https://doi.org/10.1007/978-3-642-13881-2_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13880-5

  • Online ISBN: 978-3-642-13881-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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