  Commonsense knowledge is paramount to enable intelligent systems. Typically,
it is characterized as being implicit and ambiguous, hindering thereby the
automation of its acquisition. To address these challenges, this paper presents
semantically enhanced models to enable reasoning through resolving part of
commonsense ambiguity. The proposed models enhance in a knowledge graph
embedding (KGE) framework for knowledge base completion. Experimental results
show the effectiveness of the new semantic models in commonsense reasoning.
