面向长尾实体的开放式知识丰富
Open Knowledge Enrichment for Long-tail Entities
摘要：
知识库（KBs）已逐渐成为许多AI应用程序的宝贵资产。虽然许多当前的知识库相当大，但它们被广泛认为是不完整的，特别是缺乏长尾实体的事实，例如，不太出名的人。现有的知识库扩充方法主要是对缺失的链接或缺失的值进行补齐。然而，它们只处理浓缩问题的一部分，缺乏对长尾实体的具体考虑。在本文中，我们提出了一个成熟的方法来丰富知识，预测丢失的属性和推断真实的事实，长尾实体从开放的Web。利用来自流行实体的先前知识来改进每个丰富步骤。在人工数据集和真实数据集上的实验以及与相关工作的比较表明了该方法的可行性和优越性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
