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How to Build an Ontology. Barry Smith http://ontology.buffalo.edu/smith. Options. Ontology of Experiments (proper treatment of utility classes), PATO, Upper-Level Ontologies (SUMO, DOLCE, BFO) OBO Relation Ontology GO Evidence Codes Functions, Disease

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How to Build an Ontology


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    1. How to Build an Ontology • Barry Smith • http://ontology.buffalo.edu/smith

    2. Options • Ontology of Experiments (proper treatment of utility classes), PATO, Upper-Level Ontologies (SUMO, DOLCE, BFO) • OBO Relation Ontology • GO Evidence Codes • Functions, Disease • BioPAX Level 2 Documentation – Commentary

    3. Preamble • Ontologies vs. Data Structures • adapted from Alan Rector et al., Binding Ontologies and Coding Systems to Electronic Health Records and MessageKR-MED 2006

    4. Medical IT’s odd organisational structure • Separate / independent development • Medical Ontologies / Terminologies • SNOMED, GALEN, NCI thesaurus, potential OBO Disease Ontology, etc. • Medical information models • HL7 messages • OpenEHR Archetypes

    5. Data structures and what they carry information about are different • Information models and ontologies are at different levels • The purpose of an ontology is to represent the world • The purpose of an information structure is to specify valid data structures structures to carry information about that world • To constrain the data structures to just those which a given software system can process

    6. Data structures and what they carry information about have different characteristics • All persons have a sex • However not all data structures about people have a field for sex • Information structures are intrinsically closed • Valid structures can be exhaustively and completely described (up to recursion) • Ontologies are intrinsically open • We can never describe the world completely

    7. Representing Information Models and Codes:Basic approach • An information model can be thought of asa logical theory of classes of information structures • The instances of the classes are concrete data structures - EHRs, messages, etc - carrying data about specific patients, tests, organisations, cases of disease, ...

    8. Ontologies • Ontologies represent entities in the world • Cases of diabetes • Patients • Insulin metabolism • Islet cells • The instances in data structures are data items in human artefacts • Information structures of associations and attributes, elements, etc.

    9. “ontology” Diabetes Diabetes Diabetes Diabetes Metabolicdisorder Metabolicdisorder DiabetesType 1 DiabetesType 1 DiabetesType 2 DiabetesType 2 Model of data structures in InformationModel Model of codes in InformationModel Participa-tion Observa-tion code_for_metabolic_disorder is_subcode_of DiabetesObserva-tion code_for_diabetes ONLY is_subcode_of topic is_subcode_of Type 1DiabetesObserva-tion code_for_diabetes_type_1 code_for_diabetes_type_2 ONLY diagnosis World vs. Data structure

    10. even a ‘utility’ ontology can be well-structured (OBI)

    11. National Center for Biomedical Ontology • $18.8 mill. NIH Roadmap Center • Stanford Medical Informatics • University of San Francisco Medical Center • Berkeley Drosophila Genome Project • Cambridge University Department of Genetics • The Mayo Clinic • University at Buffalo Department of Philosophy

    12. From chromosome to disease

    13. genomics • transcriptomics • proteomics • reactomics • metabonomics • phenomics • behavioromics • connectomics • toxicopharmacogenomics • bibliomics • … legacy of Human Genome Project

    14. where in the body ? what kind of disease process ?  need for semantic annotation of data

    15. how create broad-coverage semantic annotation systems for biomedicine? covering: in vitro biological phenomena model organisms humans

    16. natural language labels to make the data cognitively accessible to human beings

    17. compare: legends for maps compare: legends for maps

    18. compare: legends for cartoons

    19. ontologies are legends for data

    20. ontologies are legends for images

    21. what lesion ? what brain function ?

    22. ontologies are legends for mathematical equations xi = vector of measurements of gene i k = the state of the gene ( as “on” or “off”) θi = set of parameters of the Gaussian model ... ...

    23. The OBO Foundry Idea GlyProt MouseEcotope sphingolipid transporter activity DiabetInGene GluChem

    24. annotation using common ontologies yields integration of databases GlyProt MouseEcotope Holliday junction helicase complex DiabetInGene GluChem

    25. annotation using common ontologies can yield integration of image data

    26. annotation using common ontologies can support comparison of image data

    27. truth

    28. simple representations can be true

    29. there are true cartoons

    30. a cartoon can be a veridical representation of reality

    31. Cartographic Projection

    32. maps may be correct by reflecting topology, rather than geometry

    33. an image can be a veridical representation of reality a fully labeled image can be an even more veridical representation of reality

    34. cartoons, like maps, always have a certain threshold of granularity

    35. grain resolution

    36. grain resolution serves cognitive accessibility we transform true imagesinto true cartoons

    37. there are also true cartoon sequences

    38. Pathway diagrams are annotated dynamic cartoons

    39. pathways can be represented at different levels of granularity

    40. Joint capsule Netter

    41. Mandible and condyle movement

    42. Condyle position in fossa wrt location of disc

    43. TMJ in jaw open and closed positions

    44. Holes and Parts • Parts • • 1 head of condyle F • • 2 neck of condyle F • • 3 disc B • • 4 retrodiscal tissue B • • 7 articular eminence F • • 8 zygomatic arch F • • 10 upper head of lateral pterygoid muscle F • • 11 lower head of lateral pterygoid muscle F • Holes • • 5 lower joint compartment B • • 6 upper joint compartment B

    45. Temporomandibular Joint (TMJ) ANTERIOR from Thomas Bittner and Louis Goldberg, KR-MED 2006