%0 Conference Paper %1 10265774 %A Bless, Christof %A Baimuratov, Ildar %A Karras, Oliver %B 2023 ACM/IEEE Joint Conference on Digital Libraries (JCDL) %D 2023 %K #bpa #rank1 myown %P 155-164 %R 10.1109/JCDL57899.2023.00030 %T SciKGTeX - A LATEX Package to Semantically Annotate Contributions in Scientific Publications %X 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 work-flows 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.

@inproceedings{10265774, abstract = {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 work-flows 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.}, added-at = {2024-02-15T16:02:53.000+0100}, author = {Bless, Christof and Baimuratov, Ildar and Karras, Oliver}, biburl = {https://www.bibsonomy.org/bibtex/278243c8f7f41b471a05961a1b9566214/ldrbmrtv}, booktitle = {2023 ACM/IEEE Joint Conference on Digital Libraries (JCDL)}, doi = {10.1109/JCDL57899.2023.00030}, interhash = {bc2541b7bcdfa2504f58ac4aeacb9994}, intrahash = {78243c8f7f41b471a05961a1b9566214}, issn = {2575-8152}, keywords = {#bpa #rank1 myown}, month = {June}, pages = {155-164}, timestamp = {2024-02-15T16:13:14.000+0100}, title = {SciKGTeX - A LATEX Package to Semantically Annotate Contributions in Scientific Publications}, year = 2023 }