iLastic: Linked Data Generation Workflow and User Interface for iMinds Scholarly Data
Scholarly publishing enhances the meaning of publications by enriching them with metadata. To achieve that, ad-hoc solutions were established so far for generating Linked Data from scholarly data, entailing non-negligible implementation and maintenance costs. Therefore, even though same or complementary data may be published by different data owners of scholarly data, existing ad-hoc solutions cannot be reused, whereas general purpose solutions were neglected. In this paper, we propose a Linked Data publishing workflow which can be (i) easily adjusted and, thus reused, by different data owners to generate and publish Linked Data from their data sources, and (ii) used to align scholarly data repositories with publications content. As a proof-of-concept, the proposed workflow was applied to iMinds research institute data warehouse which was aligned with publications derived from Ghent University’s digital repository. Moreover, a user interface, which is easily adjustable and extensible, was developed to facilitate lay users to explore semantically annotated data, as the ones of the iLastic Linked Data set.
Published in 2018 in Proceedings of the Workshop on Semantics, Analytics, Visualisation: Enhancing Scholarly Data.
- RML
- scholarly data
- Linked Data
- reuse
- proof
- publication
- metadata
- research
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Cite this article easily using its BibTeX entry:
@inproceedings{dimou_savesd_2017,
author = {Dimou, Anastasia and Haesendonck, Gerald and Vanbrabant, Martin and De Vocht, Laurens and Verborgh, Ruben and Mannens, Erik},
title = {{iLastic:} {Linked Data} Generation Workflow and User Interface for {iMinds} Scholarly Data},
year = 2018,
month = oct,
booktitle = {Proceedings of the Workshop on Semantics, Analytics, Visualisation: Enhancing Scholarly Data},
editor = {Gonz\'alez-Beltr\'an, Alejandra and Osborne, Francesco and Peroni, Silvio and Vahdati, Sahar},
series = {Lecture Notes in Computer Science},
volume = 10250,
pages = {15--32},
isbn = {978-3-030-01379-0},
doi = {10.1007/978-3-030-01379-0_2},
}
Alternatively, pick a reference of your choice below:
- ACM
- Anastasia Dimou, Gerald Haesendonck, Martin Vanbrabant, Laurens De Vocht, Ruben Verborgh, and Erik Mannens. 2018. iLastic: Linked Data Generation Workflow and User Interface for iMinds Scholarly Data. In Proceedings of the Workshop on Semantics, Analytics, Visualisation: Enhancing Scholarly Data (Lecture Notes in Computer Science), 15–32.
- APA
- Dimou, A., Haesendonck, G., Vanbrabant, M., De Vocht, L., Verborgh, R., & Mannens, E. (2018). iLastic: Linked Data Generation Workflow and User Interface for iMinds Scholarly Data. In A. González-Beltrán, F. Osborne, S. Peroni, & S. Vahdati (Eds.), Proceedings of the Workshop on Semantics, Analytics, Visualisation: Enhancing Scholarly Data (Vol. 10250, pp. 15–32).
- IEEE
- A. Dimou, G. Haesendonck, M. Vanbrabant, L. De Vocht, R. Verborgh, and E. Mannens, “iLastic: Linked Data Generation Workflow and User Interface for iMinds Scholarly Data,” in Proceedings of the Workshop on Semantics, Analytics, Visualisation: Enhancing Scholarly Data, 2018, vol. 10250, pp. 15–32.
- LNCS
- Dimou, A., Haesendonck, G., Vanbrabant, M., De Vocht, L., Verborgh, R., Mannens, E.: iLastic: Linked Data Generation Workflow and User Interface for iMinds Scholarly Data. In: González-Beltrán, A., Osborne, F., Peroni, S., and Vahdati, S. (eds.) Proceedings of the Workshop on Semantics, Analytics, Visualisation: Enhancing Scholarly Data. pp. 15–32 (2018).
- MLA
- Dimou, Anastasia, et al. “ILastic: Linked Data Generation Workflow and User Interface for IMinds Scholarly Data.” Proceedings of the Workshop on Semantics, Analytics, Visualisation: Enhancing Scholarly Data, edited by Alejandra González-Beltrán et al., vol. 10250, 2018, pp. 15–32.
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