  Knowledge graph embedding models learn the representations of entities and
relations in the knowledge graphs for predicting missing links (relations)
between entities. Their effectiveness are deeply affected by the ability of
modeling and inferring different relation patterns such as symmetry, asymmetry,
inversion, composition and transitivity. Although existing models are already
able to model many of these relations patterns, transitivity, a very common
relation pattern, is still not been fully supported. In this paper, we first
theoretically show that the transitive relations can be modeled with
projections. We then propose the Rot-Pro model which combines the projection
and relational rotation together. We prove that Rot-Pro can infer all the above
relation patterns. Experimental results show that the proposed Rot-Pro model
effectively learns the transitivity pattern and achieves the state-of-the-art
results on the link prediction task in the datasets containing transitive
relations.
