Limits...
Representing annotation compositionality and provenance for the Semantic Web.

Livingston KM, Bada M, Hunter LE, Verspoor K - J Biomed Semantics (2013)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

ABSTRACT

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.

No MeSH data available.


Extended example of statement-element provenance. This figure depicts an extended example of statement-element-level provenance. One RdfStatement from each graph and the six RdfStatementElement instances have bold ovals with underlined labels. This figure is an extension of Figure 2, and some of the parts of that figure have been preserved here but grayed out. Dashed lines show assertions that can be inferred. (See the caption of Figure 1 for explanation of shapes and arrows).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4129183&req=5

Figure 4: Extended example of statement-element provenance. This figure depicts an extended example of statement-element-level provenance. One RdfStatement from each graph and the six RdfStatementElement instances have bold ovals with underlined labels. This figure is an extension of Figure 2, and some of the parts of that figure have been preserved here but grayed out. Dashed lines show assertions that can be inferred. (See the caption of Figure 1 for explanation of shapes and arrows).

Mentions: Just as RdfStatementElement instances can be based on instances of RdfResourceAnnotation, they can also be based on other instances of RdfStatementElement. As the composition of annotations becomes more complex and the layers of annotation get deeper, graph annotations will build on other graph annotations. This is especially true for annotations produced and used by computational language understanding systems [24]. For example, in Figure 4, statement element se9 is based on statement element se6, which is in turn based on resource annotation ra7. Figure 4 only shows the provenance of statement element se6 of statement s2 along with the provenance of statement element se9 of statement s3. Although not depicted, statement-element-level provenance could analogously be recorded for all elements of these statements, as well as for all statements of graph annotations ga2 and ga3. The following are triples representing annotation information for statements s2 and s3 and statement elements se6 and se9, including their provenance, rendered in Figure 4:


Representing annotation compositionality and provenance for the Semantic Web.

Livingston KM, Bada M, Hunter LE, Verspoor K - J Biomed Semantics (2013)

Extended example of statement-element provenance. This figure depicts an extended example of statement-element-level provenance. One RdfStatement from each graph and the six RdfStatementElement instances have bold ovals with underlined labels. This figure is an extension of Figure 2, and some of the parts of that figure have been preserved here but grayed out. Dashed lines show assertions that can be inferred. (See the caption of Figure 1 for explanation of shapes and arrows).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4129183&req=5

Figure 4: Extended example of statement-element provenance. This figure depicts an extended example of statement-element-level provenance. One RdfStatement from each graph and the six RdfStatementElement instances have bold ovals with underlined labels. This figure is an extension of Figure 2, and some of the parts of that figure have been preserved here but grayed out. Dashed lines show assertions that can be inferred. (See the caption of Figure 1 for explanation of shapes and arrows).
Mentions: Just as RdfStatementElement instances can be based on instances of RdfResourceAnnotation, they can also be based on other instances of RdfStatementElement. As the composition of annotations becomes more complex and the layers of annotation get deeper, graph annotations will build on other graph annotations. This is especially true for annotations produced and used by computational language understanding systems [24]. For example, in Figure 4, statement element se9 is based on statement element se6, which is in turn based on resource annotation ra7. Figure 4 only shows the provenance of statement element se6 of statement s2 along with the provenance of statement element se9 of statement s3. Although not depicted, statement-element-level provenance could analogously be recorded for all elements of these statements, as well as for all statements of graph annotations ga2 and ga3. The following are triples representing annotation information for statements s2 and s3 and statement elements se6 and se9, including their provenance, rendered in Figure 4:

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

ABSTRACT

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.

No MeSH data available.