Entity Embedding Analogy for Implicit Link Discovery

Abstract

In this work we are interested in the problem of knowledge graph (KG) incompleteness, which we propose to solve by discovering implicit triples using observed ones in the incomplete graph leveraging analogy structures deducted from a 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 to a link prediction task.