conceptual interoperability and biomedical data
DESCRIPTION
The goals of conceptual interoperability are:Make similar but distinct data resources available for search, conversion, and inter-mapping in a way that mirrors human understanding of the data being searched.Make data resources that use cross-cutting models (HL7-RIM, provenance models, etc.) interoperable with domain-specific models without explicit mappings between them.TRANSCRIPT
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Conceptual Interoperabilityand Biomedical Data
James McCuskerTetherless World Constellation,Rensselaer Polytechnic Institute
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Overview
Conceputal, logical, and physical models Use cases for conceptual interoperability Requirements for conceptual interoperability Modeling caBIG (v. 1) layered semantics in
OWL The Conceptual Model Ontology (CMO) Supporting interoperability use cases and
requirements
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Back to the Ontology Spectrum
3
Catalog/ID
SelectedLogical
Constraints(disjointness,
inverse, …)
Terms/glossary
Thesauri“narrower
term”relation
Formalis-a
Frames(properties)
Informalis-a
Formalinstance Value
Restrs.
GeneralLogical
constraints
Originally from AAAI 1999- Ontologies Panel by Gruninger, Lehmann, McGuinness, Uschold, Welty; – updated by McGuinness.Description in: www.ksl.stanford.edu/people/dlm/papers/ontologies-come-of-age-abstract.html
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Layered Modeling
Conceptual Model: An expression of a domain expert's understanding
of that domain
Logical Model: A representation of a set of logic, declarative or
procedural, that defines entities, their relations, and their properties.
Physical Model: The underlying representation structure that
actually contains the data.
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Layered ModelingExamples
Conceptual Models can be: Cmaps, high-level UML class sketches, etc.
Logical Models can be: OWL Ontologies, UML diagrams, software class
structures, etc.
Physical Model: Triple stores, SQL databases, noSQL databases,
flat files, XML files, data streams, RDF files, etc.
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Layers of Interoperability
Physical Interoperability: AKA syntactic interoperability. All the labels lign up
properly, and the structures look the same.
Logical Interoperability: All data is represented in a common model.
Conceptual Interoperability: Models expressed in a common vocabulary,
describing things that have a degree of similarity proportional to the degree of similarity of their conceptual models.
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Goals of CI
Make similar but distinct data resources available for search, conversion, and inter-mapping in a way that mirrors human understanding of the data being searched.
Make data resources that use cross-cutting models (HL7-RIM, provenance models, etc.) interoperable with domain-specific models without explicit mappings between them.
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The Promise of CI
Imagine being able to search across GEO, ArrayExpress, and caArray without writing a query for each.
Imagine being able to search for patient history across domain-specific databases using queries that only talk about patient history.
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Use case: Search
Natural language queries with controlled vocabularies:
Find me all things that are nci:TissueSpecimen with an nci:Diagnosis of nci:Melanoma.
And do this with minimal knowledge of the underlying logical model.
In fact, we want to be logical model-agnostic.
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Use case: Conversion
We should be able to lift instance data over with a certain level of fidelity data from one logical model to another.
This can be between domain models, or between a domain model and a cross-cutting model, such as a provenance model.
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Use case: Mapping
We should be able to create an automated mapping between two logical models.
For instance, take existing caBIG data models and align them with the BRIDG (Biomedical Research Integrated Domain Group) model.
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Conceptual Interoperability Requirements
Conceptual models must: use a common vocabulary that is distinct from any particular conceptual model.
A conceptual modeling framework must: support natural, idiomatic expression of the actual
data in its natural form. provide a way to express relationships between
types, properties, and relations. provide a way of expressing additional relationships
between concepts.
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Modeling caBIG (v. 1)Layered Semantics in OWL
Efforts from http://bit.ly/147FwJ resulted in additional indirection to express UML attributes:
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Modeling caBIG (v. 1)Layered Semantics in OWL
It would look like this if it were regular OWL:
This isn't possible in OWL 1, and doesn't work in OWL 2 if nci:Name and nci:Nucleic_Acid_Hybridization are owl:Classes.
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The Conceptual ModelOntology (CMO)
http://purl.org/twc/ontologies/cmo.owl
Tying classes and properties to concepts:
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Why SKOS?
Most vocabularies are already being used as terminologies, which SKOS is ideally suited for.
A skos:Concept is an Individual, and therefore can be referenced by non-OWL predicates.
Using SKOS eliminates accidental interference with logical models expressed in OWL.
Conceptual models discuss ideas (concepts), not sets (classes).
Why OWL?
I'm happy to entertain suggestions to the contrary.
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The Conceptual ModelOntology (CMO)
Describing relation edges using concepts:
And qualities
of types:
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The Conceptual ModelOntology (CMO)
Relating conceptual models to common vocabularies using simple composition tying into existing SKOS heirarchies:
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The Conceptual ModelOntology (CMO)
Behaviors are defined in terms of what they use and produce. This is more powerful than it sounds. See SADI for examples.
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CMO SatisfiesCI Requirements
✔ Common vocabularies that is distinct from any particular conceptual model
✔ Support natural, idiomatic expression of the actual data in its natural form.
✔ Not limited to caBIG models, but can be used on any logical model expressed in OWL.
✔ Provide a way to express relationships between types, properties, and relations.
✔ Provide a way of expressing additional relationships between concepts.
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CI Use Cases: Search
Find me all things that are nci:TissueSpecimen with an nci:Diagnosis of nci:Melanoma.
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CU Use Cases: Conversion
Supported using rules like:
→
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CU Use Cases: Conversion
Would be filled with this data:
→
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CU Use Cases: Mapping
We can also create class relationships:
We're experimenting with this currently.
→
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Oh, and it's working today
We've set up a RESTful service for caGrid data and models to linked data (swBIG).
http://swbig.googlecode.com Visible to linked data tools. The models already use CMO. Everything is linked, and have predictable URIs:
caDSR Model: http://purl.org/twc/cabig/model/[project]-[version].owl
Endpoint Model: http://purl.org/twc/cabig/endpoints/[endpoint].owl
List Instances: http://purl.org/twc/cabig/list/[endpoint]/[pkg].[class]
Get Instance: http://purl.org/twc/cabig/endpoints/[endpoint]/[pkg].[cls]/[id]
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Conclusions
Conceputal models can play a significant role in automated semantic interoperability.
Conceptual Model Ontology can support important uses cases in conceptual interoperability.
You can experiment with CMO-enhanced models and data today using swBIG.
Not limited to caBIG models, but can be applied to any logical model expressed in OWL.
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Thank you!