ILastic: Linked Data Generation Workflow and User Interface for iMinds Scholarly Data
| Autor: | Gerald Haesendonck, Laurens De Vocht, Steven Latre, Erik Mannens, Ruben Verborgh, Anastasia Dimou, Martin Vanbrabant |
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| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry media_common.quotation_subject 02 engineering and technology Linked data Data warehouse Set (abstract data type) World Wide Web Metadata Workflow 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Quality (business) User interface business Publication media_common |
| Zdroj: | Semantics, Analytics, Visualization ISBN: 9783030013783 |
| DOI: | 10.1007/978-3-030-01379-0_2 |
| Popis: | Enriching scholarly data with metadata enhances the publications’ meaning. Unfortunately, different publishers of overlapping or complementary scholarly data neglect general-purpose solutions for metadata and instead use their own ad-hoc solutions. This leads to duplicate efforts and entails non-negligible implementation and maintenance costs. In this paper, we propose a reusable Linked Data publishing workflow that can be easily adjusted by different data owners to (i) generate and publish Linked Data, and (ii) align scholarly data repositories with enrichments over the publications’ content. As a proof-of-concept, the proposed workflow was applied to the iMinds research institute data warehouse, which was aligned with publications’ content derived from Ghent University’s digital repository. Moreover, we developed a user interface to help lay users with the exploration of the iLastic Linked Data set. Our proposed approach relies on a general-purpose workflow. This way, we manage to reduce the development and maintenance costs and increase the quality of the resulting Linked Data. |
| Databáze: | OpenAIRE |
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