STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths
Table 3. Overall results of all variants of our methods on three datasets (in %).
['Dataset Metric', '[BOLD] Environment Acc', '[BOLD] Environment MRR', '[BOLD] Environment Wu&P', '[BOLD] Science Acc', '[BOLD] Science MRR', '[BOLD] Science Wu&P', '[BOLD] Food Acc', '[BOLD] Food MRR', '[BOLD] Food Wu&P']
The Effect of Multi-view Information. We study the contributions of different views by comparing STEAM with its variants (STEAM-D, STEAM-C, STEAM-L). As shown, it is clear that all three types of features contribute significantly to the overall performance. When eliminating one of the three views, the average performance drops by 6.07%, 8.10% and 4.67% for the three metrics.