Knowledge base completion by learning pairwise-interaction differentiated embeddings

A knowledge base of triples like (subject entity, predicate relation,object entity) is a very important resource for knowledge management. It is very useful for human-like reasoning, query expansion, question answering (Siri) and other related AI tasks. However, such a knowledge base often suffers from incompleteness due to a large volume of increasing knowledge in the real world and a lack of reasoning capability. In this paper, we propose a Pairwise-interaction Differentiated Embeddings model to embed entities and relations in the knowledge base to low dimensional vector representations and then predict the possible truth of additional facts to extend the knowledge base. In addition, we present a probability-based objective function to improve the model optimization. Finally, we evaluate the model by considering the problem of computing how likely the additional triple is true for the task of knowledge base completion. Experiments on WordNet and Freebase show the excellent performance of our model and algorithm.