PRRL: Path Rotation based Knowledge Graph Representation Learning method

Abstract

Knowledge graph (KG) representation learning aims at embedding triples in the form of vectors. Their semantic similarity can be expressed through the distance of those vectors, and thus easily be computed for further application, such as knowledge completion. Early KG embedding methods, such as the TransE model, were mainly training with relationship of each individual triple, and ignore the relationships between multiple triples. Thus their representation result is subject to the integrity of the triples. Recently, some path-enhanced methods, such as PTransE and RPJE, adopt the path information composed of multiple triples as supplement relation information for training, which achieves better effect than those triples based methods. On the other hand, the performance of path-enhanced methods is still affected by the fact that they are hard to learn the symmetric relation pattern in both triples and paths. Thus we propose Path Rotation based KG Representation Learning method (PRRL), which maps entities and relations into complex vector space and defines both relations and paths (composed by sequence of relations) as a rotation from source entity to target entity. PRRL can model and infer a variety of relation patterns, including symmetry/antisymmetry, inversion and composition relations by representing the path with Hadamard product of relations. The results of experiment on multiple datasets show that PRRL is better than the baseline in the completion of the task of KG.