Knowledge Graphs (KGs) have shown to be very important for applications such as personal assistants, question-answering systems, and search engines. Therefore, it is crucial to ensure their high quality. However, KGs inevitably contain errors, duplicates, and missing values, which may hinder their adoption and utility in business applications, as they are not curated, e.g., low-quality KGs produce low-quality applications that are built on top of them. In this vision paper, we propose a practical knowledge graph curation framework for improving the quality of KGs. First, we define a set of quality metrics for assessing the status of KGs, Second, we describe the verification and validation of KGs as cleaning tasks, Third, we present duplicate detection and knowledge fusion strategies for enriching KGs. Furthermore, we give insights and directions toward a better architecture for curating KGs.
翻译:知识图(KGs)已证明对于个人助理、问答系统和搜索引擎等应用非常重要,因此,确保质量至关重要。然而,知识图(KGs)不可避免地含有错误、重复和缺失值,可能妨碍其采用和在商业应用中发挥作用,因为知识图(KGs)没有加以整理,例如,低质量知识图(KGs)产生低质量应用程序,而这些应用程序是在上面建立的。在本愿景文件中,我们提出了一个实用的知识图整理框架,以提高KGs的质量。首先,我们确定了一套评估KGs状况的高质量指标,第二,我们将KGs的核查和验证描述为清洁任务,第三,我们提出了用于丰富KGs的重复检测和知识集成战略。此外,我们给出了更完善KGs结构的洞见和方向。