  With knowledge graphs (KGs) at the center of numerous applications such as
recommender systems and question answering, the need for generalized pipelines
to construct and continuously update such KGs is increasing. While the
individual steps that are necessary to create KGs from unstructured (e.g. text)
and structured data sources (e.g. databases) are mostly well-researched for
their one-shot execution, their adoption for incremental KG updates and the
interplay of the individual steps have hardly been investigated in a systematic
manner so far. In this work, we first discuss the main graph models for KGs and
introduce the major requirement for future KG construction pipelines. Next, we
provide an overview of the necessary steps to build high-quality KGs, including
cross-cutting topics such as metadata management, ontology development, and
quality assurance. We then evaluate the state of the art of KG construction
w.r.t the introduced requirements for specific popular KGs as well as some
recent tools and strategies for KG construction. Finally, we identify areas in
need of further research and improvement.
