Inproceedings3992

CoCon: A Data Set on Combined Contextualized Research Artifact Use

Tarek Saier, Youxiang Dong, Michael Färber

Published: 2023 Juni

Buchtitel: 2023 ACM/IEEE Joint Conference on Digital Libraries (JCDL)
Seiten: 47--50
Verlag: IEEE

Referierte Veröffentlichung

BibTeX

Kurzfassung
In the wake of information overload in academia, methodologies and systems for search, recommendation, and prediction to aid researchers in identifying relevant research are actively studied and developed. Existing work, however, is limited in terms of granularity, focusing only on the level of papers or a single type of artifact, such as data sets. To enable more holistic analyses and systems dealing with academic publications and their content, we propose CoCon, a large scholarly data set reflecting the combined use of research artifacts, contextualized in academic publications' full-text. Our data set comprises 35 k artifacts (data sets, methods, models, and tasks) and 340 k publications. We additionally formalize a link prediction task for “combined research artifact use prediction” and provide code to utilize analyses of and the development of ML applications on our data. All data and code is publicly available at https://github.com/IllDepence/contextgraph.

DOI Link: 10.1109/JCDL57899.2023.00016

Forschungsgruppe

Web Science

Forschungsgebiet