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Image Ontology. Barry Smith. Logistics. Webcasting: Karel Skinner (NIH/NIDA); Jody Sachs (NIH/NHLBI) http:// Signatures needed. Acknowledgements. To the NIH: Roadmap for Medical Research Grant 1 U 54 HG004028 

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Image Ontology

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    1. Image Ontology Barry Smith

    2. Logistics • Webcasting: Karel Skinner (NIH/NIDA); Jody Sachs (NIH/NHLBI) • http:// • Signatures needed

    3. Acknowledgements To the NIH: Roadmap for Medical Research Grant 1 U 54 HG004028

    4. Acknowledgements

    5. Image Ontology Barry Smith

    6. What this meeting is about • to promote interoperability of image and imaging ontologies in the biomedical domain • through the application of principles of sound ontology construction • through the coordination of current ontology development efforts • to promote compatibility of image ontologies with ontologies of the biomedical entities which images represent

    7. Special topics • explaining the role of a reference ontology such as the FMA • defining relations among images, features, interpretations, and the underlying reality • building an ontology of imaging tools and data • presenting the services of the National Center for Biomedical Ontology

    8. The Reality • Biomedicine is marked by gimmicky, low quality, half-finished ontologies • by incompatible, special-purpose, terminologies à la UMLS • by the assumption that data integration can be brought about by somehow ‘mapping’ ontologies built for different purposes

    9. Most ontologies (and terminologies) are execrable; but some exemplars of good practice do exist • as far as possible don’t reinvent • use the power of combination and collaboration • ontologies are like telephones: they are valuable only to the degree that they are used and networked with other ontologies • but choose working telephones • most UMLS telephones were broken from the start

    10. Why do we need rules/standards for good ontology? • Ontologies must be intelligible both to humans (for annotation) and to machines (for reasoning and error-checking): unintuitive rules for classification lead to errors • Simple, intuitive rules facilitate training of curators and annotators • Common rules allow alignment with other ontologies (and thus cross-domain exploitation of data) • Logically coherent rules enhance harvesting of content through automatic reasoning systems

    11. Ontologies built according to common logically coherent rules will make entry easier and yield a safer growth path • You can start small, annotating your data/images with initial fragments of a well-founded ontology, confident that the results will still be usable when the ontology grows larger and richer

    12. Assumptions • There are best practices in ontology development which should be followed to create stable high-quality ontologies • Shared high quality ontologies foster cross-disciplinary and cross-domain re-use of data, and create larger communities

    13. A methodology for building and testing ontologies applied thus far in the biomedical domain on: • FMA • GO + other OBO Ontologies • FuGO • SNOMED • UMLS Semantic Network • NCI Thesaurus • ICF (International Classification of Functioning, Disability and Health) • ISO Terminology Standards • HL7-RIM

    14. Biomedical science needs to find uniform computable ways of representing the reality captured in (image) data

    15. Ad hoc creation of new database schemas for each research group / research hypothesisvs. Two Strategies Pre-established interoperable stable reference ontologies in terms of which all database schemas need to be defined

    16. How to create the conditions for a step-by-step evolution towards gold standard reference ontologies in the biomedical research domain?

    17. The OBO Foundry The solution

    18. Goal of the OBO Foundry project To introduce some of the features of scientific peer review into biomedical ontology development

    19. Some OBO ontologies are of high quality Some not

    20. The OBO Foundry OBO Foundry A subset of OBO ontologies whose developers agree in advance to accept a common set of principles designed to assure • intelligibility to biologist curators, annotators, users • formal robustness • stability • compatibility • interoperability • support for logic-based reasoning

    21. The OBO Foundry OBO Foundry • OBO-UBO / Ontology of Biomedical Reality unifying framework for clinical trial database schemata • Anatomy • Pathoanatomy • Physiology • Pathophysiology • Mk. II NCI Thesaurus

    22. The OBO Foundry will provide a small reward for those doing good work in science-based ontology (analogue of peer review) It will provide a step towards the day when interoperability through controlled vocabularies can be enforced through agreements with biological research groups, clinical guidelines bodies, and scientific journals

    23. The OBO Foundry OBO Foundry • OBO-UBO / Ontology of Biomedical Reality unifying framework for clinical trial database schemata • Anatomy [FMA?] • Pathoanatomy • Physiology • Pathophysiology • Mk. II NCI Thesaurus

    24. Orthogonality Orthogonality: ontology groups who choose to be part of the OBO Core thereby commit themselves to collaborating to resolve disagreements which arise where their respective domains overlap (They commit themselves to conceiving ontology as a science, not as a hobby)

    25. Reference Ontology vs. Application Ontology A reference ontology is analogous to a scientific theory; it seeks to optimize representational adequacy to its subject matter

    26. Reference Ontology vs. Application Ontology An application ontology is comparable to an engineering artifact such as a software tool. It is constructed for specific practical purposes.

    27. Reference Ontology vs. Application Ontology Application ontologies are often built afresh for each new task; commonly introducing not only idiosyncrasies of format or logic, but also simplifications or distortions of their subject-matters. To solve this problem application ontology development shoud take place always against the background of a formally robust reference ontology framework

    28. OBO FOUNDRY EVALUATION CRITERIA Further criteria will be added over time in order to bring about a gradual improvement in the quality of ontologies included in the OBO core.

    29. The ontology is open and available to be used by all without any constraint other than (1) its origin must be acknowledged and (2) it is not to be altered and subsequently redistributed under the original name or with the same identifiers. • The ontology is in, or can be instantiated in, a common formal language. • The ontology possesses a unique identifier space within OBO. • The ontology provider has procedures for identifying distinct successive versions.

    30. The ontology has a clearly specified and clearly delineated content. • The ontology includes textual definitions for all terms. • The ontology is well-documented. • The ontology has a plurality of independent users. • The ontology uses relations which are unambiguously defined following the pattern of definitions laid down in the OBO Relation Ontology.


    32. Towards an Ontology of the (Radiological) Image

    33. Acknowledgements • Werner Ceusters • Matthew Fielding • Louis Goldberg • Dirk Marwede • Jose L. Mejino, Jr. • Cornelius Rosse

    34. Entity =def anything which exists, including things and processes, functions and qualities, beliefs and actions, software and images

    35. Representation =def an image, idea, map, picture, name, description ... which refers to, or is intended to refer to, some entity or entities in reality in what follows this ‘or is intended to refer to’ should always be assumed

    36. Ontologies are representational artifactsWe are interested in ontologies to support high-level scientific research

    37. Ontologies which support high-level scientific research are windows on reality; they are relational entities, which link cognitive agents (and computers) to entities in reality

    38. Images are representational artifacts

    39. Images, too, are windows on reality;they are relational entities, which link viewers to reality

    40. ... and they can do this even in the absence of the object

    41. What is an ontology?

    42. A representation of entities

    43. Catalog vs. inventory

    44. Catalog of Types

    45. instances types

    46. Type =Def. that which (1) a collection of similar instances share in common and which (b) is a potential object of investigation by science Types existed many trillions of years before there were words, concepts, or scientific theories

    47. Types • Types exist, through their instances, in objective reality • – including types of image, of imaging process, of brain region, of clinical procedure, of protocol, of assay, etc.

    48. Two kinds of representational artifact • Databases, inventories, images: represent what is particular in reality = instances • Ontologies, terminologies, catalogs: represent what is general in reality (exists in multiple instances) = types (universals, kinds)

    49. Images represent instances in reality

    50. Ontologies represent types in reality