Representing annotation compositionality and provenance for the Semantic Web

Background 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 knowl...

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Vydáno v:Journal of biomedical semantics Ročník 4; číslo 1; s. 38
Hlavní autoři: Livingston, Kevin M, Bada, Michael, Hunter, Lawrence E, Verspoor, Karin
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 22.11.2013
BioMed Central Ltd
Springer Nature B.V
Témata:
Algorithms
Annotations
Bioinformatics
Combinatorial Libraries
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Data Mining and Knowledge Discovery
Domains
Gene sequencing
Genomes
Knowledge
Knowledge representation
Mathematics
Mathematics and Statistics
Medical records
Medical writing
Metadata
Methods
Ontology
Programming languages
Provenance
Semantic web
Semantics
Tracking
Australia
United States
Provenance
Ontology
Conceptual data modeling
OWL
RDF
Markup
Annotation
ISSN:2041-1480, 2041-1480
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Popis
Shrnutí:Background 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. Results We 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. Conclusions With 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.
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ISSN:2041-1480
2041-1480
DOI:10.1186/2041-1480-4-38