Entity Embeddings with Conceptual Subspaces as a Basis for Plausible Reasoning
Table 7: Link prediction and Triple classification results.
['Models', 'Link Prediction (FB15k) Mean Rank', 'Link Prediction (FB15k) HITS@10', 'Triple Classification FB13', 'Triple Classification FB15k']
To a large extent, this is due to the fact that, apart from pTransE, the other models do not exploit the bag-of-words representations of the entities. Note that we do not show results for EECStext and EECSno rel in the tables because these models do not take into account any input from the knowledge graph, and is therefore not suitable. The baselines Skip-gram and CBOW are not considered for the same reason. With the exception of the pTransE variants, we again show the published results for the baseline models. On the FB13 dataset, our model matches the performance of the TransH model, although we clearly outperform all baselines on the FB15k dataset. This means that on the FB13 dataset, the bag-of-words representations of the entities cannot be exploited effectively, although our model is still not at a disadvantage.