SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions
Table 3: The MAP result of Entity Classification
['[BOLD] Metrics', 'FB15K', 'FB20K']
Results The evaluation results are listed in Tab. Notably, TransE is only triple-specific and SSP (Std.) performs as well as the NMF in the zero-shot learning. Thus they are trivial for FB20K. The observations are as follows: Overall, our method SSP yields the best accuracy, justifying the effectiveness of our method. Compared to NMF and TransE, the results show that the interactions between triples and texts make a key difference and only one of them could hardly produce an effective performance. The promotion of SSP (Joint) in FB20K demonstrates the triple embedding also has a positive effect on textual semantics. Compared to TransE, the improvements illustrate the effectiveness of semantic integration. Compared to DKRL, the promotion demonstrates the important role of modeling strong correlation.