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Image Ontology

Image Ontology. Barry Smith. 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

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Image Ontology

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  1. Image Ontology Barry Smith http://ontology.buffalo.edu

  2. 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 http://ontology.buffalo.edu

  3. 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 http://ontology.buffalo.edu

  4. 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 http://ontology.buffalo.edu

  5. 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 http://ontology.buffalo.edu

  6. 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 http://ontology.buffalo.edu

  7. 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 http://ontology.buffalo.edu

  8. 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 http://ontology.buffalo.edu

  9. 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 http://ontology.buffalo.edu

  10. Biomedical science needs to find uniform computable ways of representing the reality captured in (image) data http://ontology.buffalo.edu

  11. 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 http://ontology.buffalo.edu

  12. How to create the conditions for a step-by-step evolution towards gold standard reference ontologies in the biomedical research domain? http://ontology.buffalo.edu

  13. The OBO Foundry The solution http://ontology.buffalo.edu

  14. Goal of the OBO Foundry project To introduce some of the features of scientific peer review into biomedical ontology development http://ontology.buffalo.edu

  15. Some OBO ontologies are of high quality Some not http://ontology.buffalo.edu

  16. 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 http://ontology.buffalo.edu

  17. 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 http://ontology.buffalo.edu

  18. 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 http://ontology.buffalo.edu

  19. 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 http://ontology.buffalo.edu

  20. 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) http://ontology.buffalo.edu

  21. Reference Ontology vs. Application Ontology A reference ontology is analogous to a scientific theory; it seeks to optimize representational adequacy to its subject matter http://ontology.buffalo.edu

  22. 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. http://ontology.buffalo.edu

  23. 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 http://ontology.buffalo.edu

  24. 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. http://ontology.buffalo.edu/obofoundry http://ontology.buffalo.edu

  25. 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. http://ontology.buffalo.edu

  26. 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. http://ontology.buffalo.edu

  27. http://ontology.buffalo.edu

  28. Towards an Ontology of the (Radiological) Image http://ontology.buffalo.edu

  29. Acknowledgements • Werner Ceusters • Matthew Fielding • Louis Goldberg • Dirk Marwede • Jose L. Mejino, Jr. • Cornelius Rosse http://ontology.buffalo.edu

  30. Entity =def anything which exists, including things and processes, functions and qualities, beliefs and actions, software and images http://ontology.buffalo.edu

  31. 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 http://ontology.buffalo.edu

  32. Ontologies are representational artifactsWe are interested in ontologies to support high-level scientific research http://ontology.buffalo.edu

  33. 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 http://ontology.buffalo.edu

  34. Images are representational artifacts http://ontology.buffalo.edu

  35. Images, too, are windows on reality;they are relational entities, which link viewers to reality http://ontology.buffalo.edu

  36. ... and they can do this even in the absence of the object http://ontology.buffalo.edu

  37. What is an ontology? http://ontology.buffalo.edu

  38. A representation of entities http://ontology.buffalo.edu

  39. Catalog vs. inventory http://ontology.buffalo.edu

  40. Catalog of Types http://ontology.buffalo.edu

  41. instances types http://ontology.buffalo.edu

  42. 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 http://ontology.buffalo.edu

  43. 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. http://ontology.buffalo.edu

  44. 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) http://ontology.buffalo.edu

  45. Images represent instances in reality http://ontology.buffalo.edu

  46. Ontologies represent types in reality http://ontology.buffalo.edu

  47. Ontologies do not represent concepts in people’s heads http://ontology.buffalo.edu

  48. “lung” is not the name of a concept concepts do not stand in part_of connectedness causes treats ... relations to each other http://ontology.buffalo.edu

  49. The clinician has a cognitive representation of what is general, based on knowledge derived from textbooks http://ontology.buffalo.edu

  50. substance organism animal cat siamese types mammal frog instances http://ontology.buffalo.edu

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