A Survey on Knowledge Graph Embeddings with Literals: Which model links better Literal-ly?
Table 12: Link prediction results on FB15K-237 dataset using filtered setting.
['[BOLD] Head Prediction Models', '[BOLD] Head Prediction MR', '[BOLD] Head Prediction MRR', '[BOLD] Head Prediction Hits@1', '[BOLD] Head Prediction Hits@3', '[BOLD] Head Prediction Hits@10']
This entails that applying LiteralE to DistMult on FB15K-237 provides better performance than applying it to other baseline models. Note that the reason for DistMult-LiteralEg model to achieve the best result on FB15K-237 dataset, as comapred to FB15K, may be due to the fact that this dataset does not have any symmetric relation, i.e., DistMult already has difficulties in modeling asymmetric relations on FB15k and adding literals might introduce noise but in case of FB15K-237, incorporating literals improves DistMult because symmetric relations are removed. Regarding the two transformation functions g and glin, the function g leads to better results than glin according to the results on both dataset.