scholarly journals Data linkage in medical science using the resource description framework: the AVERT model

2018 ◽  
Vol 1 ◽  
pp. 20
Author(s):  
Brian P Reddy ◽  
Brett Houlding ◽  
Lucy Hederman ◽  
Mark Canney ◽  
Christophe Debruyne ◽  
...  

There is an ongoing challenge as to how best manage and understand ‘big data’ in precision medicine settings. This paper describes the potential for a Linked Data approach, using a Resource Description Framework (RDF) model, to combine multiple datasets with temporal and spatial elements of varying dimensionality. This “AVERT model” provides a framework for converting multiple standalone files of various formats, from both clinical and environmental settings, into a single data source. This data source can thereafter be queried effectively, shared with outside parties, more easily understood by multiple stakeholders using standardized vocabularies, incorporating provenance metadata and supporting temporo-spatial reasoning. The approach has further advantages in terms of data sharing, security and subsequent analysis. We use a case study relating to anti-Glomerular Basement Membrane (GBM) disease, a rare autoimmune condition, to illustrate a technical proof of concept for the AVERT model.

2019 ◽  
Vol 1 ◽  
pp. 20 ◽  
Author(s):  
Brian P Reddy ◽  
Brett Houlding ◽  
Lucy Hederman ◽  
Mark Canney ◽  
Christophe Debruyne ◽  
...  

There is an ongoing challenge as to how best manage and understand ‘big data’ in precision medicine settings. This paper describes the potential for a Linked Data approach, using a Resource Description Framework (RDF) model, to combine multiple datasets with temporal and spatial elements of varying dimensionality. This “AVERT model” provides a framework for converting multiple standalone files of various formats, from both clinical and environmental settings, into a single data source. This data source can thereafter be queried effectively, shared with outside parties, more easily understood by multiple stakeholders using standardized vocabularies, incorporating provenance metadata and supporting temporo-spatial reasoning. The approach has further advantages in terms of data sharing, security and subsequent analysis. We use a case study relating to anti-Glomerular Basement Membrane (GBM) disease, a rare autoimmune condition, to illustrate a technical proof of concept for the AVERT model.


Author(s):  
Christian Bizer ◽  
Maria-Esther Vidal ◽  
Michael Weiss

Author(s):  
Gbéboumé Crédo Charles Adjallah-Kondo ◽  
Zongmin Ma

As a data format, JSON is able to store and exchange data. It can be mapped with RDF (resource description framework), which is an ontology technology in the direction of web resources. This chapter replies to the question about which techniques or methods to utilize for mapping XML to JSON and RDF. However, a plethora of methods have been explored. Consequently, the goal of this survey is to give the whole presentation of the currents approaches to map JSON with XML and RDF by providing their differences.


Author(s):  
Franck Cotton ◽  
Daniel Gillman

Linked Open Statistical Metadata (LOSM) is Linked Open Data (LOD) applied to statistical metadata. LOD is a model for identifying, structuring, interlinking, and querying data published directly on the web. It builds on the standards of the semantic web defined by the W3C. LOD uses the Resource Description Framework (RDF), a simple data model expressing content as predicates linking resources between them or with literal properties. The simplicity of the model makes it able to represent any data, including metadata. We define statistical data as data produced through some statistical process or intended for statistical analyses, and statistical metadata as metadata describing statistical data. LOSM promotes discovery and the meaning and structure of statistical data in an automated way. Consequently, it helps with understanding and interpreting data and preventing inadequate or flawed visualizations for statistical data. This enhances statistical literacy and efforts at visualizing statistics.


Author(s):  
Kaleem Razzaq Malik ◽  
Tauqir Ahmad

This chapter will clearly show the need for better mapping techniques for Relational Database (RDB) all the way to Resource Description Framework (RDF). This includes coverage of each data model limitations and benefits for getting better results. Here, each form of data being transform has its own importance in the field of data science. As RDB is well known back end storage for information used to many kinds of applications; especially the web, desktop, remote, embedded, and network-based applications. Whereas, EXtensible Markup Language (XML) in the well-known standard for data for transferring among all computer related resources regardless of their type, shape, place, capability and capacity due to its form is in application understandable form. Finally, semantically enriched and simple of available in Semantic Web is RDF. This comes handy when with the use of linked data to get intelligent inference better and efficient. Multiple Algorithms are built to support this system experiments and proving its true nature of the study.


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