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.

本文提出了一种用于构建具有可调精度的知识库的方法，该方法通过概率建模用户对每个实体 - 属性对的共识，来控制知识库的精度，并介绍了三种神经网络用于拟合共识模型，并在 Google Maps 数据上进行了评估。研究表明所提出的学习模型很好地校准，并且能够成功地用于控制知识库的精度。

使用主观和事实属性构建高精度知识库

Constructing High Precision Knowledge Bases with Subjective and Factual  Attributes

Describable visual facial attributes are now commonplace in human biometrics
and affective computing, with existing algorithms even reaching a sufficient
point of maturity for placement into commercial products. These algorithms
model objective facets of facial appearance, such as hair and eye color,
expression, and aspects of the geometry of the face. A natural extension, which
has not been studied to any great extent thus far, is the ability to model
subjective attributes that are assigned to a face based purely on visual
judgements. For instance, with just a glance, our first impression of a face
may lead us to believe that a person is smart, worthy of our trust, and perhaps
even our admiration - regardless of the underlying truth behind such
attributes. Psychologists believe that these judgements are based on a variety
of factors such as emotional states, personality traits, and other physiognomic
cues. But work in this direction leads to an interesting question: how do we
create models for problems where there is no ground truth, only measurable
behavior? In this paper, we introduce a new convolutional neural network-based
regression framework that allows us to train predictive models of crowd
behavior for social attribute assignment. Over images from the AFLW face
database, these models demonstrate strong correlations with human crowd
ratings.

本文介绍了一种基于卷积神经网络的回归框架，可用于训练社交属性分配的预测模型，该模型可以对人群的行为进行量化，应用于描述人脸的视觉属性和情感计算领域。