Open Knowledge Enrichment for Long-tail Entities
This addresses the issue of incomplete knowledge bases for AI applications, particularly for less famous entities, but appears incremental as it builds on existing enrichment methods with specific considerations for long-tail entities.
The paper tackles the problem of incomplete knowledge bases, especially for long-tail entities, by proposing a full-fledged approach that predicts missing properties and infers true facts from the open Web, leveraging prior knowledge from popular entities to improve enrichment steps, with experiments on synthetic and real-world datasets demonstrating feasibility and superiority.
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.