How to Build an Ontology

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

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

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

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

20. compare: legends for maps compare: legends for maps

21. compare: legends for cartoons

22. ontologies are legends for data

23. ontologies are legends for images

24. what lesion ? what brain function ?

25. 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 ... ...

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

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

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

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

30. truth

31. simple representations can be true

32. there are true cartoons

33. a cartoon can be a veridical representation of reality

34. Cartographic Projection

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

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

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

38. grain resolution

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

40. there are also true cartoon sequences

42. Pathway diagrams are annotated dynamic cartoons

43. pathways can be represented at different levels of granularity

45. Joint capsule Netter

46. Mandible and condyle movement

47. Condyle position in fossa wrt location of disc

48. TMJ in jaw open and closed positions

49. 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

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