In pace with developments in the research field of artificial intelligence, knowledge graphs (KGs) have attracted a surge of interest from both academia and industry. As a representation of semantic relations between entities, KGs have proven to be particularly relevant for natural language processing (NLP), experiencing a rapid spread and wide adoption within recent years. Given the increasing amount of research work in this area, several KG-related approaches have been surveyed in the NLP research community. However, a comprehensive study that categorizes established topics and reviews the maturity of individual research streams remains absent to this day. Contributing to closing this gap, we systematically analyzed 507 papers from the literature on KGs in NLP. Our survey encompasses a multifaceted review of tasks, research types, and contributions. As a result, we present a structured overview of the research landscape, provide a taxonomy of tasks, summarize our findings, and highlight directions for future work.

翻译：随着人工智能研究领域的发展,知识图(KGs)吸引了学术界和工业界的兴趣,作为各实体间语义关系的一种体现,KGs已证明对自然语言处理(NLP)特别相关,近年来,这种处理迅速扩散和广泛采用。鉴于该领域研究工作的数量不断增加,在NLP研究界调查了若干与KG相关的方法。然而,直到今天,一项对既定专题进行分类并审查个别研究流成熟程度的全面研究仍未完成。为了缩小这一差距,我们系统地分析了507篇来自NLP关于KGs的文献的论文。我们的调查包括对任务、研究类型和贡献的多方面审查。结果,我们提出了对研究格局的结构性概览,对任务进行分类,总结我们的调查结果,并突出未来工作的方向。