SciKGTeX - A LATEX Package to Semantically Annotate Contributions in Scientific Publications

Abstract

The continuously increasing output of published research makes the work of researchers harder as it becomes impossible to keep track of and compare the most recent advances in a field. Scientific knowledge graphs have been proposed as a solution to structure the content of research publications in a machine-readable way and enable more efficient, computer-assisted workflows for many research activities. Crowdsourcing approaches are used frequently to build and maintain such scientific knowledge graphs. Researchers are motivated to contribute to these crowdsourcing efforts as they want their work to be included in the knowledge graphs and benefit from applications built on top of them. 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 thesis, I present SciKGTeX, a L A T E X package to semantically annotate scientific contributions at the time of document creation. The L A T E X package allows authors of scientific publications to mark the main contributions such as the background, research problem, method, results and conclusion of their work directly in L A T E X source files. The package then automatically embeds them as metadata into the generated PDF document. In addition to the package, I document a user evaluation with 26 participants which I conducted to assess the usability and feasibility of the solution.The analysis of the evaluation results shows that SciKGTeX is highly usable with a score of 79 out of 100 on the System Usability Scale. Furthermore, the study showed that the functionalities of the package can be picked up very quickly by the study participants which only needed 7 minutes on average to annotate the main contributions on a sample abstract of a published paper. SciKGTeX demonstrates a new way to generate structured metadata for the key contributions of research publications and embed them into PDF files at the time of document creation. nische Informationsbibliothek (TIB) in Hannover for allowing me to work together with his group in the context of the ORKG project. Many of the colleagues at TIB have given me great feedback on SciKGTeX and helped me to achieve my evaluation goals. I hope the outcome of this work will ultimately contribute to their vision of an open scientific knowledge graph.Lastly, I thank all the anonymous participants in the user evaluation of this work for their time and feedback. A specific thanks goes to Dr. Umutcan Şimşek for letting me present my thesis in his Seminar and gaining valuable insights as well as a few more evaluation participants.