Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs
Table 2: Zero-shot link prediction results on the unseen relations. The proposed baselines are shown at the top of the table; Our generative adversarial model is denoted as ZSGANKG and the results are shown at the bottom. Bold numbers denote the best results, and Underline numbers denote the best ones among our ZSGANKG methods.
['Model', '[BOLD] NELL-ZS MRR', '[BOLD] NELL-ZS Hits@10', '[BOLD] NELL-ZS Hits@5', '[BOLD] NELL-ZS Hits@1', '[BOLD] Wiki-ZS MRR', '[BOLD] Wiki-ZS Hits@10', '[BOLD] Wiki-ZS Hits@5', '[BOLD] Wiki-ZS Hits@1']
For NELL-ZS dataset, we set the embedding size as 100. For Wiki-ZS, we set the embedding size as 50 for faster training. The proposed generative method uses the pre-trained KG embeddings as input, which are trained on the triples in the training set. For TransE and DistMult, we directly use their 1-D vectors. For feature encoder, the upper limit of the neighbor number is 50, the number of reference triples k in one training step is 30, and the learning rate is 5e−4. For the generative model, the learning rate is 1e−4, and β1, β2 are set as 0.5, 0.9 respectively. When updating the generator one time, the iteration number nd of the discriminator is 5. The dimension of the random vector z is 15, and the number of the generated relation embedding Ntest is 20. Spectral normalization is applied for both generator and discriminator. These hyperparameters are also tuned on the validation set Dvalid. As for word embeddings, we directly use the released word embedding set GoogleNews- Even though NELL-ZS and Wiki-ZS have different scales of triples and relation sets, the proposed generative method still achieves consistent improvements over various baselines on both two zero-shot datasets. It demonstrates that the generator successfully finds the intermediate semantic representation to bridge the gap between seen and unseen relations and generates reasonable relation embeddings for unseen relations merely from their text descriptions. Therefore, once trained, our model can be used to predict arbitrary newly-added relations without fine-tuning, which is significant for real-world knowledge graph completion. 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.