Due to the lack of structure, scholarly knowledge remains hardly accessible for machines. Scholarly knowledge graphs have been proposed as a solution. Creating such a knowledge graph requires manual effort and domain experts, and is therefore time-consuming and cumbersome. In this work, we present a human-in-the-loop methodology used to build a scholarly knowledge graph leveraging literature survey articles. Survey articles often contain manually curated and high-quality tabular information that summarizes findings published in the scientific literature. Consequently, survey articles are an excellent resource for generating a scholarly knowledge graph. The presented methodology consists of five steps, in which tables and references are extracted from PDF articles, tables are formatted and finally ingested into the knowledge graph. To evaluate the methodology, 92 survey articles, containing 160 survey tables, have been imported in the graph. In total, 2,626 papers have been added to the knowledge graph using the presented methodology. The results demonstrate the feasibility of our approach, but also indicate that manual effort is required and thus underscore the important role of human experts.

中文翻译：

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从调查文章表创建学术知识图

由于缺乏结构，机器仍然很难获得学术知识。已经提出了学术知识图作为解决方案。创建这样的知识图需要人工和领域专家，因此既费时又麻烦。在这项工作中，我们提出了一种人为循环的方法，该方法用于利用文献调查文章来构建学术知识图。调查文章通常包含人工整理的高质量表格信息，这些信息汇总了科学文献中发表的发现。因此，调查文章是生成学术知识图的极佳资源。所提出的方法包括五个步骤，其中从PDF文章中提取表和参考，对表进行格式化，最后将其吸收到知识图中。为了评估该方法，已在图中导入了92个调查文章，其中包含160个调查表。使用提出的方法，总共2,626篇论文被添加到知识图中。结果证明了我们方法的可行性，但也表明需要人工来完成，因此强调了人类专家的重要作用。