Active Learning of Strict Partial Orders: A Case Study on Concept Prerequisite Relations
This work addresses the lack of large-scale labels for mining strict partial orders, such as concept prerequisite relations, with incremental improvements in active learning efficiency.
The paper tackles the problem of extracting strict partial orders from relational data by developing an active learning framework that incorporates relational reasoning to deduce labels and devise query strategies, resulting in substantially improved classification performance with the same query budget compared to baseline approaches.
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.