Improving Open Science Using Linked Open Data: CONICET Digital Use Case
Scientific publication services are changing drastically, researchers demand intelligent search services to discover and relate scientific publications. Publishers need to incorporate semantic information to better organize their digital assets and make publications more discoverable. In this paper,...
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| Autores principales: | , , , |
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| Formato: | Articulo |
| Lenguaje: | Inglés |
| Publicado: |
2019
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/74419 |
| Aporte de: |
| Sumario: | Scientific publication services are changing drastically, researchers demand intelligent search services to discover and relate scientific publications. Publishers need to incorporate semantic information to better organize their digital assets and make publications more discoverable. In this paper, we present the on-going work to publish a subset of scientific publications of CONICET Digital as Linked Open Data. The objective of this work is to improve the recovery and reuse of data through Semantic Web technologies and Linked Data in the domain of scientific publications.
To achieve these goals, Semantic Web standards and reference RDF schema’s have been taken into account (Dublin Core, FOAF, VoID, etc.). The conversion and publication process is guided by the methodological guidelines for publishing government linked data. We also outline how these data can be linked to other datasets DBLP, WIKIDATA and DBPEDIA on the web of data. Finally, we show some examples of queries that answer questions that initially CONICET Digital does not allow. |
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