Compositional Learning of Relation Paths Embedding for Knowledge Base Completion

Nowadays, large-scale knowledge bases containing billions of facts have reached impressive sizes; however, they are still far from completion. In addition, most existing methods only consider the direct links between entities, ignoring the vital impact about the semantic of relation paths. In this paper, we study the problem of how to better embed entities and relations into different low dimensional spaces. A compositional learning model of relation paths embedding (RPE) is proposed to take full advantage of additional semantic information expressed by relation paths. More specifically, using corresponding projection matrices, RPE can simultaneously embed entities into corresponding relation and path spaces. It is also suggested that type constraints could be extended from traditional relation-specific to the new proposed path-specific ones. Both of the two type constraints can be seamlessly incorporated into RPE and decrease the errors in prediction. Experiments are conducted on the benchmark datasets and the proposed model achieves significant and consistent improvements compared with the state-of-the-art algorithms for knowledge base completion.