K-fold cross-validation (CV) is a robust method for estimating generalization performance of supervised learning models. Although CV is more reliable than using a single hold-out test set, it is also
Data files containing the full outputs of all hyperparameter searches needed for TWM to simulate the KGE model ComplEx on 5 KGs -- CoDExSmall, DBpedia50 OpenEA, Kinships, and UMLS. Data is in a pre-pr
