From another point of view, two mainstreams for extracting relations in KBQA are studied: (a) using a classifier which chooses the most probable relation among all (Mohammed et al., 2018) ; (b) matching questions and relations through learning of an embedding space for representing all relations and question words (Bordes et al., 2015; Dai et al., 2016; Yin et al., 2016; He and Golub, 2016;<cite> Yu et al., 2017)</cite> , in which each relation is considered either as a meaningful sequence of words or as a unique entity.