Avoiding background knowledge: literature based discovery from important information

Preiss, J. orcid.org/0000-0002-2158-5832 (2023) Avoiding background knowledge: literature based discovery from important information. In: BMC Bioinformatics. 15th International Conference on Data and Text Mining in Biomedical Informatics (DTMBIO 2021), 22 Oct 2021, Online. Springer Science and Business Media LLC , p. 570.

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Item Type: Proceedings Paper
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© The Author(s) 2023. Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Keywords: Literature based discovery; Machine learning; Subject–predicate–object triples; Timeslicing gold standard; Knowledge Discovery; Knowledge
Dates:
  • Published: 14 March 2023
  • Published (online): 14 March 2023
  • Accepted: 16 August 2022
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 21 Mar 2023 14:42
Last Modified: 21 Mar 2023 14:42
Status: Published
Publisher: Springer Science and Business Media LLC
Refereed: Yes
Identification Number: 10.1186/s12859-022-04892-8
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