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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Unsupervised Ontology Acquisition from Plain Texts: The OntoGain System
verfasst von : Euthymios Drymonas, Kalliopi Zervanou, Euripides G. M. Petrakis
Erschienen in: Natural Language Processing and Information Systems
Verlag: Springer Berlin Heidelberg
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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.