OntoR ontology extraction system (demo + handouts)

The demo presents a semi-supervised machine learning process, where new term dictionary candidates may be found in given text, by finding closest matches in previously known ontologies. (i.e. hierarchical vocabulary, term structure, usually industry or domain specific). We show how a corpus-harvested new term can be aligned with its closest matches in an prior existing term ontology. New term's functional and semantic environment is analyzed, and the feature variables extracted are compared to values of previously known terms. The user is given the supervision control to decide the best alignment match and thus refine the ontology incrementally.