  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.
