Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2023 ACM/IEEE Joint Conference on Digital Libraries |
| Untertitel | JCDL |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 155-164 |
| Seitenumfang | 10 |
| ISBN (elektronisch) | 9798350399318 |
| ISBN (Print) | 979-8-3503-9932-5 |
| Publikationsstatus | Veröffentlicht - 2023 |
| Veranstaltung | 2023 ACM/IEEE Joint Conference on Digital Libraries, JCDL 2023 - Santa Fe, USA / Vereinigte Staaten Dauer: 26 Juni 2023 → 30 Juni 2023 |
Publikationsreihe
| Name | Proceedings of the ACM/IEEE Joint Conference on Digital Libraries |
|---|---|
| Band | 2023-June |
| ISSN (Print) | 1552-5996 |
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.
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2023 ACM/IEEE Joint Conference on Digital Libraries: JCDL. Institute of Electrical and Electronics Engineers Inc., 2023. S. 155-164 (Proceedings of the ACM/IEEE Joint Conference on Digital Libraries; Band 2023-June).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - SciKGTeX
T2 - 2023 ACM/IEEE Joint Conference on Digital Libraries, JCDL 2023
AU - Bless, Christof
AU - Baimuratov, Ildar
AU - Karras, Oliver
N1 - Funding Information: The authors would like to thank the Federal Government and the Heads of Government of the Linder, as well as the Joint Science Conference (GWK), for their funding and support within the framework of the NFDI4Ing consortium. This work was partially funded by the German Research Foundation (DFG) - project number 442146713, by the European Research Council for the project ScienceGRAPH (Grant agreement ID: 819536), and by the TIE ~ Leibniz Information Centre for Science and Technology.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - FAIR data
KW - LATEX
KW - Scientific Knowledge Graphs
KW - Semantic Annotation
UR - http://www.scopus.com/inward/record.url?scp=85164571232&partnerID=8YFLogxK
U2 - 10.15488/12462
DO - 10.15488/12462
M3 - Conference contribution
AN - SCOPUS:85164571232
SN - 979-8-3503-9932-5
T3 - Proceedings of the ACM/IEEE Joint Conference on Digital Libraries
SP - 155
EP - 164
BT - 2023 ACM/IEEE Joint Conference on Digital Libraries
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 June 2023 through 30 June 2023
ER -