Knowledge Transfer for Out-of-Knowledge-Base Entities:A Graph Neural Network Approach
Table 4: Results of the OOKB experiment: accuracy of the simple baseline and proposed models. Bold and underlined figures are respectively the best and second best scores for each dataset.
['Method', 'Pooling', 'Head 1,000', 'Head 3,000', 'Head 5,000', 'Tail 1,000', 'Tail 3,000', 'Tail 5,000', 'Both 1,000', 'Both 3,000', 'Both 5,000']
The column labeled “pooling” indicates which pooling function was used. As the table shows, our model outperforms the baselines considerably. In particular, Graph-NN with average pooling outperforms the other methods on all datasets. Graph-NN with max pooling also shows good accuracy, but in several settings, such as Tail-3000, it was outperformed by the baseline model with average pooling.