Efficient learning of entity and predicate embeddings for link prediction in knowledge graphs

Knowledge Graphs are a widely used formalism for representing knowledge in the Web of Data. We focus on the problem of predicting missing links in large knowledge graphs, so to discover new facts about the world. Recently, representation learning models that embed entities and predicates in continuous vector spaces achieved new state-of-the-art results on this problem. A major limitation in these models is that the training process, which consists in learning the optimal entity and predicate embeddings for a given knowledge graph, can be very computationally expensive: it may even require days of computations for large knowledge graphs. In this work, by leveraging adaptive learning rates, we propose a principled method for reducing the training time by an order of magnitude, while learning more accurate link prediction models. Furthermore, we employ the proposed training method for evaluating a set of novel and scalable models. Our evaluations show significant improvements over state-of-the-art link prediction methods on the WORDNET and FREEBASE datasets.