A Survey on Knowledge Graph Embeddings with Literals: Which model links better Literal-ly?
Table 14: MRR results on link prediction task on YAGO-10 taken from MKBE [mmkb:emnlp18].
['[BOLD] YAGO-10 Models', '[BOLD] YAGO-10 MRR', '[BOLD] YAGO-10 Hits@1', '[BOLD] YAGO-10 Hits@3', '[BOLD] YAGO-10 Hits@10']
Note that it is not possible to compare the whole of MKBE [mmkb:emnlp18] with any other model as it is the only embedding model which utilizes the three types of literals together: text, numeric, and images. Therefore, its sub model S+I which uses only images has been compared with the embedding model IKRL [Xie2017ImageembodiedKR]. They have compared the models DistMult+S+I, ConvE+S+I, and IKRL where S stands for structure and I for Image. Both DistMult+S+I and ConvE+S+I are sub models of MKBE which use only relational triples and Images. The result indicates that ConvE+S+I outperforms the other two models in all metrics on the YAGO-10 dataset (refer to MKBE [mmkb:emnlp18] for more details).