Using OWL for Rhetorical Ontology Building
The Web ontology language, OWL, presents opportunities and challenges for the cognitive representation of rhetorical figures. OWL is a description-logic–based knowledge representation language in which subsumption (“IsA”= hypernymy/hyponomy) is the organizing relationship. However, OWL’s reliance on subsumption as the primary semantic relation forces different sorts of relations to be translated into subsumption relations, but other common relations, e.g., sequence, can’t be represented in any natural way. Our research team is developing innovative ways, through linked data and SWRL rules, to meet the challenges while capitalizing on the opportunities provided by OWL’s automated classification and consistency-checking.
Di Marco will consider two key movements (1) Linked Linguistic Open Data (LLOD): the linking of linguistic and semantic resources in a common Semantic Web format (e.g., Web Ontology Language (OWL)); and (2) Combining ontologies with Machine Learning in Natural Language Processing. She will address two related questions: (1) Whether the RhetFig Ontology, which uses OWL format, might be usefully linked with other LLOD resources, and (2) Whether Machine Learning might one day be integrated with annotated rhetorical corpora (when such corpora eventually exist).