Scientific knowledge graphs have been proposed as a solution to structure the content of research publications in a machine-actionable way and enable more efficient, computer-assisted workflows for many research activities. Crowd-sourcing approaches are used frequently to build and maintain such scientific knowledge graphs. To contribute to scientific knowledge graphs, researchers need simple and easy-to-use solutions to generate new knowledge graph elements and establish the practice of semantic representations in scientific communication. In this paper, we present a workflow for authors of scientific documents to specify their contributions with a LaTeX package, called SciKGTeX, and upload them to a scientific knowledge graph. The SciKGTeX package allows authors of scientific publications to mark the main contributions of their work directly in LaTeX source files. The package embeds marked contributions as metadata into the generated PDF document, from where they can be extracted automatically and imported into a scientific knowledge graph, such as the ORKG. This workflow is simpler and faster than current approaches, which make use of external web interfaces for data entry. Our user evaluation shows that SciKGTeX is easy to use, with a score of 79 out of 100 on the System Usability Scale, as participants of the study needed only 7 minutes on average to annotate the main contributions on a sample abstract of a published paper. Further testing shows that the embedded contributions can be successfully uploaded to ORKG within ten seconds. SciKGTeX simplifies the process of manual semantic annotation of research contributions in scientific articles. Our workflow demonstrates how a scientific knowledge graph can automatically ingest research contributions from document metadata.

翻译：科学知识图谱被提出作为一种以机器可读方式结构化研究出版物内容的解决方案，能够为许多研究活动支持更高效的计算机辅助工作流程。众包方法常用于构建和维护此类科学知识图谱。为助力科学知识图谱的建设，研究人员需要简单易用的工具来生成新知识图谱元素，并在科学交流中建立语义表征的实践。本文提出一套面向科学文献作者的工作流程，允许他们通过名为SciKGTeX的LaTeX宏包指定其贡献，并将其上传至科学知识图谱。该宏包使科学出版物作者能够直接在LaTeX源文件中标记其主要贡献，并将标记的贡献以元数据形式嵌入生成的PDF文档中，随后可自动提取并导入至科学知识图谱（如ORKG）。相比当前使用外部网页界面进行数据输入的方法，本工作流程更简单快速。用户评估显示，SciKGTeX易用性较高，在系统可用性量表上获得79分（满分100分），且研究参与者平均仅需7分钟即可完成对一篇已发表论文样例摘要主要贡献的标注。进一步测试表明，嵌入的贡献可在10秒内成功上传至ORKG。SciKGTeX简化了科研文献中研究贡献的手动语义标注流程，我们的工作流程验证了科学知识图谱如何从文档元数据中自动采集研究贡献。