A Study on Automated Relation Labelling in Ontology Learning
Martin Kavalec · 2005
Ontology learning from texts has been proposed as a technology helping ontology designers in the modelling process. Within ontology learning, the discovery of non-taxonomic relations is understood as the problem least addressed. We propose a technique for extraction of lexical items that may give cue in assigning semantic labels to otherwise ‘anonymous’ non-taxonomic relations. The technique has been implemented as extension to the existing Text-to-Onto tool. Experiments have been carried out on a collection of texts describing tour destinations as well as on a semantically annotated general corpus. The paper also discusses evaluation aspects of relation labelling, among which the distinction of prior and posterior precision looks as most important.