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To enable a performance evaluation comparison with the supervised method, we developed a rule-based algorithm that handles the full frame and FEs annotation. The main intuition is to map FEs defined in the frame repository to ontology classes of the target KB: such mapping serves as a set of rule pairs (FE, class), e.g., (WINNER , SoccerClub). In the FrameNet terminology, this is homologous to the assignment of semantic types to FEs: for instance, in the ACTIVITY frame, the AGENT is typed with the generic class Sentient. The idea would allow the implementation of the bottom-up one-step annotation flow described in [18]: to achieve this, we run EL over the input sentences and check whether the attached ontology class metadata appear in the frame repository, thus fulfilling the FE classification task.
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