  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.
