Learning to Cite: Transfer Learning for Digital Archives
We consider the problem of automatically creating citations for digital archives. We focus on the learning to cite framework that allows us to create citations without users or experts in the loop. In this work, we study the possibility of learning a citation model on one archive and then applying the model to another archive that has never been seen before by the system.
The work was partially funded by the “Computational Data Citation” (CDC) STARS-StG project of the University of Padua.
- 1.Task Group on Data Citation Standards and Practices, Out of Cite, Out of Mind: The Current State of Practice, Policy, and Technology for the Citation of Data, vol. 12. CODATA-ICSTI, September 2013Google Scholar
- 2.Alawini, A., Chen, L., Davidson, S.B., Portilho Da Silva, N., Silvello, G.: Automating data citation: the eagle-i experience. In: ACM/IEEE Joint Conference on Digital Libraries, JCDL 2017, pp. 169–178. IEEE Computer Society (2017)Google Scholar
- 4.Buneman, P., Silvello, G.: A rule-based citation system for structured and evolving datasets. IEEE Data Eng. Bull. 33(3), 33–41 (2010)Google Scholar
- 5.FORCE-11: Data Citation Synthesis Group: Joint Declaration of Data Citation Principles. FORCE11, San Diego, CA, USA (2014)Google Scholar
- 7.Silvello, G.: A methodology for citing linked open data subsets. D-Lib Magazine 21(1/2) (2015). https://doi.org/10.1045/january2015-silvello
- 11.Wu, Y., Alawini, A., Davidson, S.B., Silvello, G.: Data citation: giving credit where credit is due. In: Proceedings of the 2018 SIGMOD Conference, pp. 99–114. ACM Press, New York (2018). https://doi.org/10.1145/3183713