  Automatic knowledge graph construction aims to manufacture structured human
knowledge. To this end, much effort has historically been spent extracting
informative fact patterns from different data sources. However, more recently,
research interest has shifted to acquiring conceptualized structured knowledge
beyond informative data. In addition, researchers have also been exploring new
ways of handling sophisticated construction tasks in diversified scenarios.
Thus, there is a demand for a systematic review of paradigms to organize
knowledge structures beyond data-level mentions. To meet this demand, we
comprehensively survey more than 300 methods to summarize the latest
developments in knowledge graph construction. A knowledge graph is built in
three steps: knowledge acquisition, knowledge refinement, and knowledge
evolution. The processes of knowledge acquisition are reviewed in detail,
including obtaining entities with fine-grained types and their conceptual
linkages to knowledge graphs; resolving coreferences; and extracting entity
relationships in complex scenarios. The survey covers models for knowledge
refinement, including knowledge graph completion, and knowledge fusion. Methods
to handle knowledge evolution are also systematically presented, including
condition knowledge acquisition, condition knowledge graph completion, and
knowledge dynamic. We present the paradigms to compare the distinction among
these methods along the axis of the data environment, motivation, and
architecture. Additionally, we also provide briefs on accessible resources that
can help readers to develop practical knowledge graph systems. The survey
concludes with discussions on the challenges and possible directions for future
exploration.
