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Accessing scientific data through knowledge graphs with Ontop

In this tutorial, we learn how to set up and exploit the virtual knowledge graph (VKG) approach to access data stored in relational legacy systems and to enrich such data with domain knowledge coming from different heterogeneous (biomedical) resources. The VKG approach is based on an ontology that d...

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Autores principales: Calvanese, Diego, Lanti, Davide, Mendes De Farias, Tarcisio, Mosca, Alessandro, Xiao, Guohui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8515008/
https://www.ncbi.nlm.nih.gov/pubmed/34693372
http://dx.doi.org/10.1016/j.patter.2021.100346
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author Calvanese, Diego
Lanti, Davide
Mendes De Farias, Tarcisio
Mosca, Alessandro
Xiao, Guohui
author_facet Calvanese, Diego
Lanti, Davide
Mendes De Farias, Tarcisio
Mosca, Alessandro
Xiao, Guohui
author_sort Calvanese, Diego
collection PubMed
description In this tutorial, we learn how to set up and exploit the virtual knowledge graph (VKG) approach to access data stored in relational legacy systems and to enrich such data with domain knowledge coming from different heterogeneous (biomedical) resources. The VKG approach is based on an ontology that describes a domain of interest in terms of a vocabulary familiar to the user and exposes a high-level conceptual view of the data. Users can access the data by exploiting the conceptual view, and in this way they do not need to be aware of low-level storage details. They can easily integrate ontologies coming from different sources and can obtain richer answers thanks to the interaction between data and domain knowledge.
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spelling pubmed-85150082021-10-21 Accessing scientific data through knowledge graphs with Ontop Calvanese, Diego Lanti, Davide Mendes De Farias, Tarcisio Mosca, Alessandro Xiao, Guohui Patterns (N Y) Tutorial In this tutorial, we learn how to set up and exploit the virtual knowledge graph (VKG) approach to access data stored in relational legacy systems and to enrich such data with domain knowledge coming from different heterogeneous (biomedical) resources. The VKG approach is based on an ontology that describes a domain of interest in terms of a vocabulary familiar to the user and exposes a high-level conceptual view of the data. Users can access the data by exploiting the conceptual view, and in this way they do not need to be aware of low-level storage details. They can easily integrate ontologies coming from different sources and can obtain richer answers thanks to the interaction between data and domain knowledge. Elsevier 2021-10-08 /pmc/articles/PMC8515008/ /pubmed/34693372 http://dx.doi.org/10.1016/j.patter.2021.100346 Text en © 2021 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Tutorial
Calvanese, Diego
Lanti, Davide
Mendes De Farias, Tarcisio
Mosca, Alessandro
Xiao, Guohui
Accessing scientific data through knowledge graphs with Ontop
title Accessing scientific data through knowledge graphs with Ontop
title_full Accessing scientific data through knowledge graphs with Ontop
title_fullStr Accessing scientific data through knowledge graphs with Ontop
title_full_unstemmed Accessing scientific data through knowledge graphs with Ontop
title_short Accessing scientific data through knowledge graphs with Ontop
title_sort accessing scientific data through knowledge graphs with ontop
topic Tutorial
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8515008/
https://www.ncbi.nlm.nih.gov/pubmed/34693372
http://dx.doi.org/10.1016/j.patter.2021.100346
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