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.


Example of statement-element provenance. This figure depicts an example of statement-element-level provenance. The RdfStatement and the three RdfStatementElement instances have bold ovals and underlined labels. This figure is an extension of Figure 1, 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 3: Example of statement-element provenance. This figure depicts an example of statement-element-level provenance. The RdfStatement and the three RdfStatementElement instances have bold ovals and underlined labels. This figure is an extension of Figure 1, 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: In RDF, the typical way to make statements about statements is to reify the statement itself as an instance of rdf:Statement. An RDF statement identifies its subject, property, and object via the relations rdf:subject, rdf:property, and rdf:object, respectively. However, RDF statements and their elements are conceptual representations; for example, in Figure 1, the RDF statement t2 hasSubjectDependent t1 represents the assertion that token t2 has as its subject token t1. To explicitly represent RDF statements as information content entities, we introduce the class kiao:RdfStatement, which is rdfs:subClassOf iao:information content entity. An example of a reified kiao:RdfStatement is the instance s1 in Figure 3. A graph annotation can then be connected to each reified statement of the graph annotation using the property obo:has_part.


Representing annotation compositionality and provenance for the Semantic Web.

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

Example of statement-element provenance. This figure depicts an example of statement-element-level provenance. The RdfStatement and the three RdfStatementElement instances have bold ovals and underlined labels. This figure is an extension of Figure 1, 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 3: Example of statement-element provenance. This figure depicts an example of statement-element-level provenance. The RdfStatement and the three RdfStatementElement instances have bold ovals and underlined labels. This figure is an extension of Figure 1, 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: In RDF, the typical way to make statements about statements is to reify the statement itself as an instance of rdf:Statement. An RDF statement identifies its subject, property, and object via the relations rdf:subject, rdf:property, and rdf:object, respectively. However, RDF statements and their elements are conceptual representations; for example, in Figure 1, the RDF statement t2 hasSubjectDependent t1 represents the assertion that token t2 has as its subject token t1. To explicitly represent RDF statements as information content entities, we introduce the class kiao:RdfStatement, which is rdfs:subClassOf iao:information content entity. An example of a reified kiao:RdfStatement is the instance s1 in Figure 3. A graph annotation can then be connected to each reified statement of the graph annotation using the property obo:has_part.

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.