1. 1 Introduction to Ontology: Terminology Barry Smith http://ontology.buffalo.edu/smith with thanks to Werner Ceusters, Waclaw Kusnierczyk, Daniel Schober

2. 2 Problem of ensuring sensible cooperation in a massively interdisciplinary community concept type instance model representation data

3. 3 What do these mean? ‘conceptual data model’ ‘semantic knowledge model’ ‘reference information model’ ‘an ontology is a specification of a conceptualization’

4. 4 natural language labels to make the data cognitively accessible to human beings and algorithmically tractable

5. 5 ontologies are legends for data

6. 6 computationally tractable legends help human beings find things in very large complex representations of reality

7. 7 Glue-ability / integration rests on the existence of a common benchmark called ‘reality’ the ontologies we want to glue together are representations of what exists in the world not of what exists in the heads of different groups of people

8. 8 maps may be correct by reflecting topology, rather than geometry

9. 9 if you’re going to semantically annotate piles of data, better work out how to do it right from the start

10. 10 two kinds of annotations

11. 11 names of types

12. 12 names of instances

13. 13 First basic distinction type vs. instance (science text vs. diary) (human being vs. Tom Cruise)

14. 14 For ontologies it is generalizations that are important = ontologies are about types, kinds, universals

15. 15 Ontology types Instances

16. 16 Ontology = A Representation of types

17. 17 An ontology is a representation of types We learn about types in reality from looking at the results of scientific experiments in the form of scientific theories experiments relate to what is particular science describes what is general

18. 18 There are created types bicycle steering wheel aspirin Ford Pinto we learn about these by looking at manufacturers’ catalogues

19. 19 measurement units are created types

20. 20 Inventory vs. Catalog Two kinds of representational artifact Very roughly: Databases represent instances Ontologies represent types

21. 21 A515287DC3300 Dust Collector Fan B521683Gilmer Belt C521682Motor Drive Belt Catalog vs. inventory

22. 22 Catalog vs. inventory

23. 23 Catalog of types/Types

24. 24 siamese mammal cat organism object types animal frog instances

25. 25 Ontologies are here

26. 26 or here

27. 27 ontologies represent general structures in reality (leg)

28. 28 Ontologies do not represent concepts in people’s heads

29. 29 They represent types in reality

30. 30 which provide the benchmark for integration

31. 31 Entity =def anything which exists, including things and processes, functions and qualities, beliefs and actions, documents and software (Levels 1, 2 and 3)

32. 32 what are the kinds of entity?

33. 33 First basic distinction type vs. instance (science text vs. diary) (human being vs. Tom Cruise)

34. 34 Ontology Types Instances

35. 35 Ontology = A Representation of types

36. 36 Domain =def a portion of reality that forms the subject- matter of a single science or technology or mode of study or administrative practice...; proteomics HIV epidemiology

37. 37 Representation =def an image, idea, map, picture, name or description... of some entity or entities.

38. 38 Ontologies are representational artifacts comparable to science texts and subject to the same sorts of constraints (including need for update)

39. 39 Representational units =def terms, icons, alphanumeric identifiers... which refer, or are intended to refer, to entities and which are minimal (atoms)

40. 40 Composite representation =def representation (1) built out of representational units which (2) form a structure that mirrors, or is intended to mirror, the entities in some domain

41. 41 Analogue representations no representational units, no ‘atoms’

42. 42 Periodic Table The Periodic Table

43. 43 Language has the power to create general terms which go beyond the domain of types studied by science and documented in catalogs

44. 44 Problem: fiat demarcations male over 30 years of age with family history of diabetes abnormal curvature of spine participant in trial #2030

45. 45 Problem: roles fist patient FDA-approved drug

46. 46 Administrative ontologies often need to go beyond types Fall on stairs or ladders in water transport injuring occupant of small boat, unpowered Railway accident involving collision with rolling stock and injuring pedal cyclist Nontraffic accident involving motor-driven snow vehicle injuring pedestrian

47. 47 Class =def a maximal collection of particulars determined by a general term (‘cell’. ‘electron’ but also: ‘ ‘restaurant in Palo Alto’, ‘Italian’) the class A = the collection of all particulars x for which ‘x is A’ is true

48. 48 types vs. their extensions types {a,b,c,...} collections of particulars

49. 49 Extension =def The extension of a type A is the class: instance of the type A (it is the class of A’s instances) (the class of all entities to which the term ‘A’ applies)

50. 50 Problem The same general term can be used to refer both to types and to collections of particulars. Consider: HIV is an infectious retrovirus HIV is spreading very rapidly through Asia

51. 51 types vs. classes types {c,d,e,...} classes

52. 52 types vs. classes types ~ defined classes

53. 53 types vs. classes types e.g. populations,...

54. 54 Defined class =def a class defined by a general term which does not designate a type the class of all diabetic patients in Leipzig on 4 June 1952

55. 55 OWL is a good representation of defined classes sibling of Finnish spy member of Abba aged > 50 years pizza with > 4 different toppings

56. 56 Terminology =def. a representational artifact whose representational units are natural language terms (with IDs, synonyms, comments, etc.) which are intended to designate types together with defined classes, with no particular attention to composite representations

57. 57 types, classes, concepts types defined classes ‘concepts’ ?

58. 58 types < defined classes < ‘concepts’ ‘concepts’ which do not correspond to defined classes: ‘Surgical or other procedure not carried out because of patient's decision’ ‘Congenital absent nipple’ because they do not correspond to anything

59. 59 (Scientific) Ontology =def. a representational artifact whose representational units (which may be drawn from a natural or from some formalized language) are intended to represent 1. types in reality 2. those relations between these types which obtain typely (= for all instances) lung is_a anatomical structure lobe of lung part_of lung

60. Rules for Scientific Ontology How ontology development can be evidence-based 60

61. Basis in textbook science OBO Foundry ontologies are created by biologist-curators with a thorough knowledge of the underlying science Ontology quality is measured in terms of biological accuracy and usefulness to working biologists (measured in turn by numbers of independent users, of associated software applications, papers published,... ). 61

62. Measure of success for OBO Foundry initiative = degree to which it serves the integration of ever more heterogeneous types of data / is exploited in the creation of new types of software or of new types of informatics- based experimentation 62

63. Ontology building closely tied to needs of users with data to annotate In the GO/Uniprot collaboration, the Foundry methodology is applied by domain experts who enjoy joint control of ontology, data and annotations. All three get to be curated in tandem. As results of experiments are described in annotations, this leads to extensions or corrections of the ontology, which in turn lead to better annotations, the whole process being governed by the querying needs of users in a way which fosters widespread adoption. Blake J, et al. Gene Ontology annotations: Proceedings of Bio-Ontologies Workshop, ISMB/ECCB, Vienna, July 20, 2007 63

64. Science-based vs. arms-length ontology This yields superior outcomes when measured by the results achieved by third parties who apply the ontologies to tasks external to those for which they were created superior = to those generated on the basis of arms-length methodologies such as automatic mining from published literature. PLoS Biol. 2005 Feb;3(2):e65. 64

65. 65 Some arguments against Will it scale? (Tools are following success, as in the case of the GO) Are we ready? (This is empirical science) Is medical classification not conventional? (methodology of fiat boundaries) Where will we get the data? (NIH policies address this problem; rich datasets available at manysites) Who will do the annotation? (Benchmark-based tools will advance automatic annotation, credit for authorship will advance human annotation)

66. OWL and OBO Description Logic Linear representation First Order Logic (SUMO, DOLCE) BFO (Basic Formal Ontology) 66