Compositional Learning of Relation Path Embedding for Knowledge Base Completion
This work addresses the incompleteness of large-scale knowledge bases for AI applications, offering an incremental improvement by extending existing methods to include relation paths.
The paper tackled the problem of knowledge base completion by incorporating the semantics of relation paths, not just direct links, and proposed a compositional learning model (RPE) that embeds entities into two latent spaces with path-specific constraints, achieving significant and consistent improvements over state-of-the-art algorithms.
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.