Building Memory with Concept Learning Capabilities from Large-scale Knowledge Base
Table 3: Link prediction results on FB15k-desc.
['[BOLD] Model', '[BOLD] Mean rank Left', '[BOLD] Mean rank Right', '[BOLD] Mean rank Avg', '[BOLD] Hits@10 (%) Left', '[BOLD] Hits@10 (%) Right', '[BOLD] Hits@10 (%) Avg']
The type of concept learning module we use here is CNN. Note that all the triplets in training, validation and test sets of FB15k-desc are the same as FB15k , so we list TransE’s results on FB15k in the same table. Compared to TransE which cannot make use of information in descriptions, our model performs much better, in terms of both mean rank and hits@10. As stated in Section 4.1, all entities in the test set of FB15k are contained in the training set, which, together with the results, shows that our framework well regularizes the embedding model by forcing embeddings to reflect information from natural language descriptions. We demonstrate the concept learning capability in the next subsection.