Learning Well-Founded Ontologies through Word Sense Disambiguation

Foundational Ontologies help maintaining and expanding ontologies expressivity power, thus enabling them to be more precise and free of ambiguities. The use of modeling languages based on these ontologies, such as OntoUML, requires not only the modeler's experience regarding such languages, but also a good understanding about the domain being modeled. Aiming to facilitate, or even enable the modeling of complex domains, several techniques have been proposed in order to automatically generate ontologies from texts. However, none is able to generate well-founded ontologies (which are constructed based on Foundational Ontologies). Moreover, an important issue on learning from text is how to distinguish among different meanings of a word, which impacts on concepts expressed by the ontologies. Therefore, techniques for word sense disambiguation must be considered. This paper proposes a technique for automatically learn well-founded ontologies described in OntoUML through word sense disambiguation.