One paradigm in proposed approaches for relation extraction in KBQA is based on semantic parsing in which questions were parsed and turned into logical forms in order to query the knowledge base (Berant et al., 2013; Berant and Liang, 2014) . 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;<cite> Yin et al., 2016</cite>; He and Golub, 2016; Yu et al., 2017) , in which each relation is considered either as a meaningful sequence of words or as a unique entity.