Active Learning of Strict Partial Orders: A Case Study on Concept Prerequisite Relations

• DOI: 10.48550/arxiv.1801.06481
• Publication Date: 2018-01-01
• Authors: Chen Liang; Jianbo Ye; Han Zhao; Bart Pursel; C. Lee Giles
• Keywords: Baseline (sea); Statistical relational learning

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