The proliferation of open knowledge graphs has led to a surge in scholarly research on the topic over the past decade. This paper presents a bibliometric analysis of the scholarly literature on open knowledge graphs published between 2013 and 2023. The study aims to identify the trends, patterns, and impact of research in this field, as well as the key topics and research questions that have emerged. The work uses bibliometric techniques to analyze a sample of 4445 scholarly articles retrieved from Scopus. The findings reveal an ever-increasing number of publications on open knowledge graphs published every year, particularly in developed countries (+50 per year). These outputs are published in highly-referred scholarly journals and conferences. The study identifies three main research themes: (1) knowledge graph construction and enrichment, (2) evaluation and reuse, and (3) fusion of knowledge graphs into NLP systems. Within these themes, the study identifies specific tasks that have received considerable attention, including entity linking, knowledge graph embedding, and graph neural networks.

翻译：开放知识图谱的兴起推动了过去十年间该领域学术研究的蓬勃发展。本文对2013年至2023年间发表的开放知识图谱学术文献进行了文献计量分析，旨在识别该领域的研究趋势、模式与影响力，以及涌现的核心主题与研究问题。通过运用文献计量技术，本研究分析了从Scopus数据库获取的4445篇学术论文样本。研究结果揭示了开放知识图谱出版物数量的逐年攀升态势，尤其在发达国家中尤为显著（年均增长超过50篇）。这些成果多发表于高引用的学术期刊与会议。研究归纳出三大核心主题：（1）知识图谱构建与增强，（2）评估与复用，（3）知识图谱与NLP系统的融合。在此框架下，研究识别出受高度关注的特定任务，包括实体链接、知识图谱嵌入及图神经网络。