Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs
Table 3: Quantitative analysis of the generated relation embeddings by our generator. These presented relations are from the NELL test relation set. “# Can. Num.” denotes the number of candidates of test relations. For one relation, “# Cos. Sim.” denotes the mean cosine similarity between the corresponding generated relation embedding and the cluster center xrc of the relation triples.
['Relations', '# Can. Num.', '# Cos. Sim.', 'MRR ZSGAN [ITALIC] KG', 'MRR ZS-DistMult', 'Hits@10 ZSGAN [ITALIC] KG', 'Hits@10 ZS-DistMult']
Unlike image, our generated data cannot be observed intuitively. Instead, we calculate the cosine similarity between the generated relation embeddings and the cluster center xrc of their corresponding relations. It can be seen that our method indeed generates the plausible relation embeddings for many relations and the link prediction performance is positively correlated with the quality of the relation embeddings.