  Large-scale knowledge bases have currently reached impressive sizes; however,
these knowledge bases are still far from complete. In addition, most of the
existing methods for knowledge base completion only consider the direct links
between entities, ignoring the vital impact of the consistent semantics of
relation paths. In this paper, we study the problem of how to better embed
entities and relations of knowledge bases into different low-dimensional spaces
by taking full advantage of the additional semantics of relation paths, and we
propose a compositional learning model of relation path embedding (RPE).
Specifically, with the corresponding relation and path projections, RPE can
simultaneously embed each entity into two types of latent spaces. It is also
proposed that type constraints could be extended from traditional
relation-specific constraints to the new proposed path-specific constraints.
The results of experiments show that the proposed model achieves significant
and consistent improvements compared with the state-of-the-art algorithms.
