Framester: A Wide Coverage Linguistic Linked Data Hub.

Semantic web applications leveraging NLP can benefit from easy access to expressive lexical resources such as FrameNet. However, the usefulness of FrameNet is affected by its limited coverage and non-standard semantics. The access to existing linguistic resources is also limited because of poor connectivity among them. We present some strategies based on Linguistic Linked Data to broaden FrameNet coverage and formal linkage of lexical and factual resources. We created a novel resource, Framester, which acts as a hub between FrameNet, WordNet, VerbNet, BabelNet, DBpedia, Yago, DOLCE-Zero, as well as other resources. Framester is not only a strongly connected knowledge graph, but also applies a rigorous formal treatment for Fillmore's frame semantics, enabling full-fledged OWL querying and reasoning on a large frame-based knowledge graph. We also describe Word Frame Disambiguation, an application that reuses Framester data as a base in order to perform frame detection from text, with results comparable in precision to the state of the art, but with a much higher coverage.