  Equipping machines with comprehensive knowledge of the world's entities and
their relationships has been a long-standing goal of AI. Over the last decade,
large-scale knowledge bases, also known as knowledge graphs, have been
automatically constructed from web contents and text sources, and have become a
key asset for search engines. This machine knowledge can be harnessed to
semantically interpret textual phrases in news, social media and web tables,
and contributes to question answering, natural language processing and data
analytics. This article surveys fundamental concepts and practical methods for
creating and curating large knowledge bases. It covers models and methods for
discovering and canonicalizing entities and their semantic types and organizing
them into clean taxonomies. On top of this, the article discusses the automatic
extraction of entity-centric properties. To support the long-term life-cycle
and the quality assurance of machine knowledge, the article presents methods
for constructing open schemas and for knowledge curation. Case studies on
academic projects and industrial knowledge graphs complement the survey of
concepts and methods.
