In this work we are interested in the problem of knowledge graphs (KG) incompleteness that we propose to solve by discovering implicit triples using observed ones in the incomplete graph leveraging analogy structures deducted from KG embedding model. We use a language modelling approach that we adapt to entities and relations. The first results show that analogical inferences in the projected vector space is relevant for link prediction task.