  Knowledge bases (KBs) are the backbone of many ubiquitous applications and
are thus required to exhibit high precision. However, for KBs that store
subjective attributes of entities, e.g., whether a movie is "kid friendly",
simply estimating precision is complicated by the inherent ambiguity in
measuring subjective phenomena. In this work, we develop a method for
constructing KBs with tunable precision--i.e., KBs that can be made to operate
at a specific false positive rate, despite storing both difficult-to-evaluate
subjective attributes and more traditional factual attributes. The key to our
approach is probabilistically modeling user consensus with respect to each
entity-attribute pair, rather than modeling each pair as either True or False.
Uncertainty in the model is explicitly represented and used to control the KB's
precision. We propose three neural networks for fitting the consensus model and
evaluate each one on data from Google Maps--a large KB of locations and their
subjective and factual attributes. The results demonstrate that our learned
models are well-calibrated and thus can successfully be used to control the
KB's precision. Moreover, when constrained to maintain 95% precision, the best
consensus model matches the F-score of a baseline that models each
entity-attribute pair as a binary variable and does not support tunable
precision. When unconstrained, our model dominates the same baseline by 12%
F-score. Finally, we perform an empirical analysis of attribute-attribute
correlations and show that leveraging them effectively contributes to reduced
uncertainty and better performance in attribute prediction.
