Unsupervised ontology acquisition from plain texts: the OntoGain system

Authors:
Euthymios Drymonas;Kalliopi Zervanou;Euripides G. M. Petrakis
Affiliations:
Intelligent Systems Laboratory, Electronic and Computer Engineering Dept., Technical University of Crete, Chania, Crete, Greece;Tilburg centre for Creative Computing, University of Tilburg, The Netherlands;Intelligent Systems Laboratory, Electronic and Computer Engineering Dept., Technical University of Crete, Chania, Crete, Greece
Venue:
NLDB'10 Proceedings of the Natural language processing and information systems, and 15th international conference on Applications of natural language to information systems
Year:
2010

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Abstract

We propose OntoGain, a system for unsupervised ontology acquisition from unstructured text which relies on multiword 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.