Knowledge bases (KBs) have gradually become a valuable asset for many AI applications. While many current KBs are quite large, they are widely acknowledged as incomplete, especially lacking facts of long-tail entities, e.g., less famous persons. Existing approaches enrich KBs mainly on completing missing links or filling missing values. However, they only tackle a part of the enrichment problem and lack specific considerations regarding long-tail entities. In this paper, we propose a full-fledged approach to knowledge enrichment, which predicts missing properties and infers true facts of long-tail entities from the open Web. Prior knowledge from popular entities is leveraged to improve every enrichment step. Our experiments on the synthetic and real-world datasets and comparison with related work demonstrate the feasibility and superiority of the approach.
@article{arxiv.2002.06397,
title = {Open Knowledge Enrichment for Long-tail Entities},
author = {Ermei Cao and Difeng Wang and Jiacheng Huang and Wei Hu},
journal= {arXiv preprint arXiv:2002.06397},
year = {2020}
}
Comments
Accepted by the 29th International World Wide Web Conference (WWW 2020)