. . . . "To fulfill both Wikidata and DBpedia duties, we aim at investigating in what extent can the Frame Semantics theory [16,17] be leveraged to perform Information Extraction over Web documents. The main purpose of Information Extraction is to gather structured data from free text via Natural Language Processing (NLP), while Frame Semantics originates from linguistic research in Artificial Intelligence. A frame can be informally defined as an event triggered by some term in a text and embedding a set of participants, or Frame Elements (FEs). Hence, the aforementioned sentence would induce the DEFEAT frame (triggered by lost) together with the WINNER, COMPETITION, and SCORE participants. Such theory has led to the creation of FRAME NET [5,6], namely a lexical database with manually annotated examples of frame usage in English. FrameNet currently adheres to a rigorous protocol for data annotation and quality control. The activity is known to be expensive with respect to time and cost, thus constituting an encumbrance for the extension of the resource [4], both in terms of additional labeled sentences and of languages. To alleviate this, crowdsourcing the annotation task is proven to dramatically reduce the financial and temporal expenses. Consequently, we foresee to exploit the novel annotation approach described in [18], which provides full frame annotation in a single step and in a bottom-up fashion, thus being also more compliant with the definition of frames as per [17]." . . . . "2019-11-10T12:34:11+01:00"^^ . .