Correcting scientific knowledge in a general-purpose ontology

Abstract

General-purpose ontologies (e.g. WordNet) are convenient, but they are not always scientifically valid. We draw on techniques from semantic class learning to improve the scientific validity of WordNet's physics forces hyponym (IS-A) hierarchy for use in an intelligent tutoring system. We demonstrate the promise of a web-based approach which gathers web statistics used to relabel the forces as scientifically valid or scientifically invalid. Our results greatly improve the F1 for predicting scientific invalidity, with small improvements in F1 for predicting scientific validity and in overall accuracy compared to the WordNet baseline. © 2010 Springer-Verlag.