Demand-Weighted Completeness Prediction for a Knowledge Base
Table 3: Usage-weighted results for the three models on the three datasets.
['Model', 'Jaccard', 'False Neg.', 'False Pos.']
6.1.4 Weighted Evaluation This gives more usage-focused evaluation, emphasizing the non-uniform usage of different class signatures. The D3large neural model achieves 85% precision with a weighted evaluation. With the low rate of false negatives, this indicates that a similar model could be used to predict the necessary relations for KB usage.