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：过去十年来，开放知识图谱的普及引发了该领域学术研究的激增。本文对2013年至2023年间发表的开放知识图文学术文献进行了文献计量分析。本研究旨在识别该领域的研究趋势、模式及影响力，同时厘清涌现的关键主题与研究问题。通过文献计量方法对Scopus数据库中检索的4445篇学术论文样本进行分析，研究结果揭示：开放知识图谱的年度发文量逐年持续增长，尤其在发达国家（年均增幅超50篇）。这些成果发表于高影响力学术期刊与会议。研究识别出三大核心主题：（1）知识图谱构建与丰富、（2）评估与复用、（3）知识图谱与自然语言处理系统的融合。在这些主题下，实体链接、知识图谱嵌入与图神经网络等具体任务获得了显著关注。