Negative Statements Considered Useful
Table 4. Ranking metrics evaluation results for peer-based inference.
['[BOLD] Ranking Model', '[BOLD] Coverage(%)', '[ITALIC]  [BOLD] n [BOLD] D [BOLD] C [BOLD] G [BOLD] 3', '[ITALIC]  [BOLD] n [BOLD] D [BOLD] C [BOLD] G [BOLD] 5', '[ITALIC]  [BOLD] n [BOLD] D [BOLD] C [BOLD] G [BOLD] 10', '[ITALIC]  [BOLD] n [BOLD] D [BOLD] C [BOLD] G [BOLD] 20']
As one can see, our ensemble outperforms the best baseline by 6 to 16% in NDCG. The coverage column reflects the percentage of statements that this model was able to score. For example, for the Popularity of Object metric, a universally negative statement will not be scored. The same goes for TransE and HolE, where 11% of the results, on average, are universally negative statements. Ranking with the Ensemble and ranking using the Frequency of Property proved to better than all other ranking metrics and the three baselines, with an improvement over the random baseline of 20% for k=3 and k=5.