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.

本文论述如何为知识图谱的建设和持续更新提供通用管道，谈论了高质量 KG 的必需步骤，包括元数据管理，本体开发和质量保证，并对 KG 建设工具和策略的现状进行评估和总结，并提出了需要进一步研究和改进的领域。