The effect of rule injection in a leakage free datasets

Knowledge graph embedding (KGE) has become a prominent topic for many AI-based tasks such as recommendation systems, natural language processing, and link prediction. Inclusion of additional knowledge such as ontology, logical rules and text improves the learning process of KGE models. One of the main characteristics of knowledge graphs (KGs) is the existence of relational patterns (e.g., symmetric and inverse relations) which usually remain unseen by the embedding models. Inclusion of logical rules provides embedding models with additional information about the patterns already present in the KGs. The injection of logical rules has not yet been studied in depth for KGE models. In this paper, we propose an approach for rule-based learning on top of the two embedding models namely RotatE and TransE within this scope of the paper. We first study the effect of rule injection in the performance of the selected models. Second, we explore how the removal of leakage from popular KGs such as FB15k and WN18 affects the results. By leakage we are referring to the patterns exist in the training set from the test set (e.g. if the test set contains (h, r, t) then it also contains t, r, h in the training set which is considered as a symmetric leakage where t, r and h refers to tail, relation and head respectively). Empirical results suggest that incorporation of logical rules in the training process improves the performance of KGE models.