A Comprehensive Survey on Automatic Knowledge Graph Construction

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