General purpose knowledge bases such as DBpedia and Wikidata are valuable resources for various AI tasks. They describe real-world facts as entities and relations between them and they are typically incomplete. Knowledge base completion refers to the task of adding new missing links between entities to build new triples. In this work, we propose an approach for discovering implicit triples using observed ones in the incomplete graph leveraging analogy structures deducted from a knowledge graph embedding model. We use a neural language modelling approach where semantic regularities between words are preserved, which we adapt to entities and relations. We consider domain specific views from large input graphs as the basis for the training, which we call context graphs, as a reduced and meaningful context for a set of entities from a given domain. Results show that analogical inferences in the projected vector space is relevant to a link prediction task in domain knowledge bases.