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Infrastructure and Population of the OpenBiodiv Biodiversity Knowledge Graph

BACKGROUND: OpenBiodiv is a biodiversity knowledge graph containing a synthetic linked open dataset, OpenBiodiv-LOD, which combines knowledge extracted from academic literature with the taxonomic backbone used by the Global Biodiversity Information Facility. The linked open data is modelled accordin...

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Detalles Bibliográficos
Autores principales: Dimitrova, Mariya, Senderov, Viktor E, Georgiev, Teodor, Zhelezov, Georgi, Penev, Lyubomir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Pensoft Publishers 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8486731/
https://www.ncbi.nlm.nih.gov/pubmed/34690512
http://dx.doi.org/10.3897/BDJ.9.e67671
Descripción
Sumario:BACKGROUND: OpenBiodiv is a biodiversity knowledge graph containing a synthetic linked open dataset, OpenBiodiv-LOD, which combines knowledge extracted from academic literature with the taxonomic backbone used by the Global Biodiversity Information Facility. The linked open data is modelled according to the OpenBiodiv-O ontology integrating semantic resource types from recognised biodiversity and publishing ontologies with OpenBiodiv-O resource types, introduced to capture the semantics of resources not modelled before. NEW INFORMATION: We introduce the new release of the OpenBiodiv-LOD attained through information extraction and modelling of additional biodiversity entities. It was achieved by further developments to OpenBiodiv-O, the data storage infrastructure and the workflow and accompanying R software packages used for transformation of academic literature into Resource Description Framework (RDF). We discuss how to utilise the LOD in biodiversity informatics and give examples by providing solutions to several competency questions. We investigate performance issues that arise due to the large amount of inferred statements in the graph and conclude that OWL-full inference is impractical for the project and that unnecessary inference should be avoided.