  This paper introduces TakeFive, a new semantic role labeling method that
transforms a text into a frame-oriented knowledge graph. It performs dependency
parsing, identifies the words that evoke lexical frames, locates the roles and
fillers for each frame, runs coercion techniques, and formalises the results as
a knowledge graph. This formal representation complies with the frame semantics
used in Framester, a factual-linguistic linked data resource. The obtained
precision, recall and F1 values indicate that TakeFive is competitive with
other existing methods such as SEMAFOR, Pikes, PathLSTM and FRED. We finally
discuss how to combine TakeFive and FRED, obtaining higher values of precision,
recall and F1.
