Title |
RDF2Graph a tool to recover, understand and validate the ontology of an RDF resource
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Published in |
Journal of Biomedical Semantics, October 2015
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DOI | 10.1186/s13326-015-0038-9 |
Pubmed ID | |
Authors |
Jesse CJ van Dam, Jasper J Koehorst, Peter J Schaap, Vitor AP Martins dos Santos, Maria Suarez-Diez |
Abstract |
Semantic web technologies have a tremendous potential for the integration of heterogeneous data sets. Therefore, an increasing number of widely used biological resources are becoming available in the RDF data model. There are however, no tools available that provide structural overviews of these resources. Such structural overviews are essential to efficiently query these resources and to assess their structural integrity and design, thereby strengthening their use and potential. Here we present RDF2Graph, a tool that automatically recovers the structure of an RDF resource. The generated overview allows to create complex queries on these resources and to structurally validate newly created resources. RDF2Graph facilitates the creation of complex queries thereby enabling access to knowledge stored across multiple RDF resources. RDF2Graph facilitates creation of high quality resources and resource descriptions, which in turn increases usability of the semantic web technologies. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Canada | 1 | 20% |
Netherlands | 1 | 20% |
Unknown | 3 | 60% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 4 | 80% |
Scientists | 1 | 20% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 2 | 5% |
United States | 2 | 5% |
Norway | 1 | 3% |
Japan | 1 | 3% |
Netherlands | 1 | 3% |
Unknown | 32 | 82% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 14 | 36% |
Professor > Associate Professor | 4 | 10% |
Student > Master | 4 | 10% |
Student > Ph. D. Student | 4 | 10% |
Student > Doctoral Student | 2 | 5% |
Other | 5 | 13% |
Unknown | 6 | 15% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 12 | 31% |
Agricultural and Biological Sciences | 10 | 26% |
Biochemistry, Genetics and Molecular Biology | 6 | 15% |
Medicine and Dentistry | 2 | 5% |
Environmental Science | 1 | 3% |
Other | 2 | 5% |
Unknown | 6 | 15% |