Conceptual Interoperability and Biomedical Data

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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.

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Conceptual Interoperability and Biomedical Data

  1. 1. Conceptual Interoperability and Biomedical Data James McCusker Tetherless World Constellation, Rensselaer Polytechnic Institute
  2. 2. 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
  3. 3. Back to the Ontology Spectrum Thesauri Selected “narrower Formal Frames Logical ConstraintsCatalog/ term” is-a (properties)(disjointness,ID relation inverse, …) Terms/ Informal Formal General Value Logical glossary is-a instance Restrs. 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 3
  4. 4. Layered ModelingConceptual Model: An expression of a domain experts understanding of that domainLogical 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.
  5. 5. Layered Modeling ExamplesConceptual 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.
  6. 6. Layers of InteroperabilityPhysical 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.
  7. 7. Goals of CIMake similar but distinct data resourcesavailable for search, conversion, and inter-mapping in a way that mirrors humanunderstanding of the data being searched.Make data resources that use cross-cuttingmodels (HL7-RIM, provenance models, etc.)interoperable with domain-specific modelswithout explicit mappings between them.
  8. 8. The Promise of CIImagine being able to search across GEO,ArrayExpress, and caArray without writing aquery for each.Imagine being able to search for patient historyacross domain-specific databases usingqueries that only talk about patient history.
  9. 9. Use case: SearchNatural language queries with controlledvocabularies: Find me all things that are nci:TissueSpecimen with an nci:Diagnosis of nci:Melanoma.And do this with minimal knowledge of theunderlying logical model.In fact, we want to be logical model-agnostic.
  10. 10. Use case: ConversionWe should be able to lift instance data over witha certain level of fidelity data from one logicalmodel to another.This can be between domain models, orbetween a domain model and a cross-cuttingmodel, such as a provenance model.
  11. 11. Use case: MappingWe should be able to create an automatedmapping between two logical models.For instance, take existing caBIG data modelsand align them with the BRIDG (BiomedicalResearch Integrated Domain Group) model.
  12. 12. Conceptual Interoperability RequirementsConceptual 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.
  13. 13. Modeling caBIG (v. 1) Layered Semantics in OWLEfforts from http://bit.ly/147FwJ resulted inadditional indirection to express UML attributes:
  14. 14. Modeling caBIG (v. 1) Layered Semantics in OWL It would look like this if it were regular OWL:This isnt possible in OWL 1, and doesnt work in OWL 2if nci:Name and nci:Nucleic_Acid_Hybridization are owl:Classes.
  15. 15. The Conceptual Model Ontology (CMO)http://purl.org/twc/ontologies/cmo.owlTying classes and properties to concepts:
  16. 16. 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? Im happy to entertain suggestions to the contrary.
  17. 17. The Conceptual Model Ontology (CMO)Describing relation edges using concepts:And qualitiesof types:
  18. 18. The Conceptual Model Ontology (CMO)Relating conceptual models to commonvocabularies using simple composition tyinginto existing SKOS heirarchies:
  19. 19. The Conceptual Model Ontology (CMO)Behaviors are defined in terms of what they useand produce. This is more powerful than itsounds. See SADI for examples.
  20. 20. CMO Satisfies CI 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.
  21. 21. CI Use Cases: SearchFind me all things that are nci:TissueSpecimenwith an nci:Diagnosis of nci:Melanoma.
  22. 22. CU Use Cases: ConversionSupported using rules like: →
  23. 23. CU Use Cases: ConversionWould be filled with this data: →
  24. 24. CU Use Cases: MappingWe can also create class relationships: →Were experimenting with this currently.
  25. 25. Oh, and its working todayWeve set up a RESTful service for caGrid dataand 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]
  26. 26. 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.
  27. 27. Thank you!

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