  Strict partial order is a mathematical structure commonly seen in relational
data. One obstacle to extracting such type of relations at scale is the lack of
large-scale labels for building effective data-driven solutions. We develop an
active learning framework for mining such relations subject to a strict order.
Our approach incorporates relational reasoning not only in finding new
unlabeled pairs whose labels can be deduced from an existing label set, but
also in devising new query strategies that consider the relational structure of
labels. Our experiments on concept prerequisite relations show our proposed
framework can substantially improve the classification performance with the
same query budget compared to other baseline approaches.

Machine Learning;Artificial Intelligence;Machine Learning