Most work on ontology learning from text relies on unsupervised methods for relation extraction inspired by Hearst’s work, and attempts to extract relations identified in work in formal linguistics and ontology. In this paper we present work aiming at extracting from text the set of concept attributes actually associated to concepts according to psychological research, and using state-of-the art supervised relation extraction techniques.
Supervised relation extraction for ontology learning from text based on a cognitively plausible model of relations.
Giuliano, Claudio;Romano, Lorenza
2008-01-01
Abstract
Most work on ontology learning from text relies on unsupervised methods for relation extraction inspired by Hearst’s work, and attempts to extract relations identified in work in formal linguistics and ontology. In this paper we present work aiming at extracting from text the set of concept attributes actually associated to concepts according to psychological research, and using state-of-the art supervised relation extraction techniques.File in questo prodotto:
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