Abstract
AbstractThe rapid generation of large amounts of information about the coronavirus SARS-CoV-2 and the disease COVID-19 makes it increasingly difficult to gain a comprehensive overview of current insights related to the disease. With this work, we aim to support the rapid access to a comprehensive data source on COVID-19 targeted especially at researchers. Our knowledge graph, CovidPubGraph, an RDF knowledge graph of scientific publications, abides by the Linked Data and FAIR principles. The base dataset for the extraction is CORD-19, a dataset of COVID-19-related publications, which is updated regularly. Consequently, CovidPubGraph is updated biweekly. Our generation pipeline applies named entity recognition, entity linking and link discovery approaches to the original data. The current version of CovidPubGraph contains 268,108,670 triples and is linked to 9 other datasets by over 1 million links. In our use case studies, we demonstrate the usefulness of our knowledge graph for different applications. CovidPubGraph is publicly available under the Creative Commons Attribution 4.0 International license.
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability
Reference31 articles.
1. Wang, L. L. et al. CORD-19: the covid-19 open research dataset. CoRR abs/2004.10706 (2020).
2. Ngomo, A.-C. N., Auer, S., Lehmann, J. & Zaveri, A. Introduction to linked data and its lifecycle on the web. In Reasoning Web International Summer School, 1–99 (Springer, 2014).
3. Wilkinson, M. D. et al. The fair guiding principles for scientific data management and stewardship. Scientific data 3 (2016).
4. Bühmann, L., Lehmann, J. & Westphal, P. Dl-learner - A framework for inductive learning on the semantic web. J. Web Semant. 39, 15–24 (2016).
5. Heindorf, S. et al. Evolearner: Learning description logics with evolutionary algorithms. In WWW (ACM, 2022).
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献