Evidence map›Paper›PMID 24268021›Full record

ArticleJournal of biomedical semantics2013

Representing annotation compositionality and provenance for the Semantic Web.

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

Abstract read
In one paragraph

Article in Journal of biomedical semantics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Kevin M LivingstonDepartment of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Michael BadaDepartment of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Lawrence E HunterDepartment of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Karin VerspoorNational ICT Australia, Victoria Research Laboratory, Melbourne, VIC, 3010, Australia ; Department of Computing and Information Systems, The University of Melbourne, Melbourne 3010 VIC, Australia.

Funding

Bio Text NLPR01LM009254 · NLM · UNIVERSITY OF COLORADO DENVER · PI HUNTER, LAWRENCE E · 2006 to 2018
$5.9M
NLM NIH HHS R01 LM009254
6 · The paper itself

Abstract

backgroundThough 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.

resultsWe present a task- and domain-independent ontological model for capturing annotations and their linkage to their denoted knowledge representations, which can be singular concepts or more complex sets of assertions. We have implemented this model as an extension of the Information Artifact Ontology in OWL and made it freely available, and we show how it can be integrated with several prominent annotation and provenance models. We present several application areas for the model, ranging from linguistic annotation of text to the annotation of disease-associations in genome sequences.

conclusionsWith this model, progressively more complex annotations can be composed from other annotations, and the provenance of compositional annotations can be represented at the annotation level or at the level of individual elements of the RDF triples composing the annotations. This in turn allows for progressively richer annotations to be constructed from previous annotation efforts, the precise provenance recording of which facilitates evidence-based inference and error tracking.

Indexed as

AnnotationConceptual data modelingMarkupOntologyOWLProvenanceRDF

Identifiers

PMID24268021
PMCPMC4129183

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.