A Survey on Knowledge Graph Embeddings with Literals: Which model links better Literal-ly?
Table 7: The number of entities, object relations, data relations, relational triples, train sets, valid sets, and test sets of the FB15K and the FB15K-237 datasets.
['[EMPTY]', '[BOLD] Datasets  [BOLD] FB15K', '[BOLD] Datasets  [BOLD] FB15K-237']
The performance of the aforementioned models was measured using two of the most commonly used datasets for link prediction, i.e., FB15K [bordes2013translating] and FB15K-237 [toutanova-chen-2015-observed] are considered. FB15K is a subset of Freebase [bollacker2008freebase] which mostly contains triples describing the facts about movies, actors, awards, sports and sport teams. It contains a randomly split training, validation, and test sets. The issue with this dataset is that the test set contains a large number of triples which are obtained by simply inverting triples in the training set. This enables a simple embedding model which is symmetric with respect to the head and tail entity to obtain an excellent performance. In order to avoid this, the dataset FB15K-237 has been created by removing the inverse relations from FB15K. The focus lies on the common entity descriptions, i.e., for those entities existing in FB15K but not in FB15K-237 no description is used, because there has already been experiments done using the whole entity descriptions for FB15K dataset in the original paper. This way it would be possible to analyse the effect of the size of the dataset (the entity descriptions) on the performance of the embedding models. The average number of words (tokens) in the descriptions is 143 whereas the maximum and minimum are 804 and 2.