Το work with title Unsupervised ontology acquisition from plain texts: the OntoGain System by Petrakis Evripidis, Euthymios Drymonas, Zervanou Kalliopi is licensed under Creative Commons Attribution 4.0 International
Bibliographic Citation
Euthymis Drymonas, Kalliope Zervanou, Euripides G.M. Petrakis, "Unsupervised Ontology Acquisition from Plain Texts: the OntoGain System", in 14th Intern. Conference Applications of Natural Language to Information Systems (NLDB'2010), 2010, pp. 277-287. doi:10.1007/978-3-642-13881-2_29
https://doi.org/10.1007/978-3-642-13881-2_29
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.