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The main research challenge is formulated as a KB population problem: specifically, we tackle how to au- tomatically enrich DBpedia resources with novel state- ments extracted from the text of Wikipedia articles. We conceive the solution as a machine learning task implementing the Frame Semantics linguistic theory [16,17]: we investigate how to recognize meaningful factual parts given a natural language sentence as input. We cast this as a classification activity falling into the su- pervised learning paradigm. Specifically, we focus on the construction of a new extractor, to be integrated into the current DBpedia infrastructure. Frame Semantics will enable the discovery of relations that hold between entities in raw text. Its implementation takes as input a collection of documents from Wikipedia (i.e., the corpus) and outputs a structured dataset composed of machine-readable statements.
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http://dx.doi.org/10.3233/SW-170269
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2019-11-10T12:34:11+01:00
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