Crowd-sourcing Web knowledge for metadata extraction

Abstract

We explore a new metadata extraction framework without human annotators with the ground truth harvested from Web. A new training sample is selected based on not only the uncertainty and representativeness in the unlabeled pool, but also on its availability and credibility in Web knowledge bases. We construct a dataset of 4329 books with valid metadata and evaluate our approach using 5 Web book databases as oracles. Empirical results demonstrate its effectiveness and efficiency.