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Semantic web data warehousing for caGrid

Overview of attention for article published in BMC Bioinformatics, January 2009
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Title
Semantic web data warehousing for caGrid
Published in
BMC Bioinformatics, January 2009
DOI 10.1186/1471-2105-10-s10-s2
Pubmed ID
Authors

James P McCusker, Joshua A Phillips, Alejandra Beltrán, Anthony Finkelstein, Michael Krauthammer

Abstract

The National Cancer Institute (NCI) is developing caGrid as a means for sharing cancer-related data and services. As more data sets become available on caGrid, we need effective ways of accessing and integrating this information. Although the data models exposed on caGrid are semantically well annotated, it is currently up to the caGrid client to infer relationships between the different models and their classes. In this paper, we present a Semantic Web-based data warehouse (Corvus) for creating relationships among caGrid models. This is accomplished through the transformation of semantically-annotated caBIG Unified Modeling Language (UML) information models into Web Ontology Language (OWL) ontologies that preserve those semantics. We demonstrate the validity of the approach by Semantic Extraction, Transformation and Loading (SETL) of data from two caGrid data sources, caTissue and caArray, as well as alignment and query of those sources in Corvus. We argue that semantic integration is necessary for integration of data from distributed web services and that Corvus is a useful way of accomplishing this. Our approach is generalizable and of broad utility to researchers facing similar integration challenges.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 7%
Brazil 2 3%
United Kingdom 2 3%
Sweden 1 1%
Netherlands 1 1%
Austria 1 1%
Iceland 1 1%
Switzerland 1 1%
Unknown 59 81%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 21%
Student > Ph. D. Student 14 19%
Student > Master 9 12%
Other 7 10%
Student > Bachelor 6 8%
Other 20 27%
Unknown 2 3%
Readers by discipline Count As %
Computer Science 35 48%
Agricultural and Biological Sciences 14 19%
Medicine and Dentistry 11 15%
Engineering 4 5%
Linguistics 2 3%
Other 5 7%
Unknown 2 3%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 September 2013.
All research outputs
#2,015,245
of 3,632,582 outputs
Outputs from BMC Bioinformatics
#1,583
of 2,289 outputs
Outputs of similar age
#47,938
of 92,245 outputs
Outputs of similar age from BMC Bioinformatics
#51
of 74 outputs
Altmetric has tracked 3,632,582 research outputs across all sources so far. This one is in the 26th percentile – i.e., 26% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,289 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 21st percentile – i.e., 21% of its peers scored the same or lower than it.
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We're also able to compare this research output to 74 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.