↓ Skip to main content

Representing annotation compositionality and provenance for the Semantic Web

Overview of attention for article published in Journal of Biomedical Semantics, November 2013
Altmetric Badge

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
37 Mendeley
citeulike
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Representing annotation compositionality and provenance for the Semantic Web
Published in
Journal of Biomedical Semantics, November 2013
DOI 10.1186/2041-1480-4-38
Pubmed ID
Authors

Kevin M Livingston, Michael Bada, Lawrence E Hunter, Karin Verspoor

Abstract

Though the annotation of digital artifacts with metadata has a long history, the bulk of that work focuses on the association of single terms or concepts to single targets. As annotation efforts expand to capture more complex information, annotations will need to be able to refer to knowledge structures formally defined in terms of more atomic knowledge structures. Existing provenance efforts in the Semantic Web domain primarily focus on tracking provenance at the level of whole triples and do not provide enough detail to track how individual triple elements of annotations were derived from triple elements of other annotations.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 3 8%
United States 2 5%
Netherlands 1 3%
United Kingdom 1 3%
Australia 1 3%
Mexico 1 3%
Brazil 1 3%
Unknown 27 73%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 27%
Student > Ph. D. Student 5 14%
Student > Master 5 14%
Student > Bachelor 4 11%
Other 3 8%
Other 7 19%
Unknown 3 8%
Readers by discipline Count As %
Computer Science 16 43%
Agricultural and Biological Sciences 6 16%
Engineering 3 8%
Arts and Humanities 2 5%
Neuroscience 2 5%
Other 4 11%
Unknown 4 11%