论文标题
长尾实体的开放知识丰富
Open Knowledge Enrichment for Long-tail Entities
论文作者
论文摘要
知识库（KB）已逐渐成为许多AI应用程序的宝贵资产。尽管许多当前的KB很大，但他们被广泛认为是不完整的，尤其是缺乏长尾实体的事实，例如不太有名的人。现有方法主要是在完成缺失的链接或填充缺失值时丰富了KB。但是，他们只解决了富集问题的一部分，并且对长尾实体缺乏具体考虑。在本文中，我们提出了一种全面的知识丰富方法，该方法预测了缺失的属性，并从开放式网络中提出了长尾实体的真实事实。利用受欢迎实体的先验知识来改善每个富集步骤。我们对合成和现实数据集的实验以及与相关工作的比较证明了该方法的可行性和优越性。
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.
