1 Towards a Reference Terminology for Talking about Ontologies and Related Artifacts Barry Smith with thanks to Werner Ceusters, Waclaw Kusnierczyk, Daniel Schober

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

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

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

6 compare: legends for maps

7 ontologies are legends for data

8 compare: legends for cartoons

9 legends help human beings use and understand complex representations of reality help human beings create useful complex representations of reality help computers process complex representations of reality

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

11 x i = vector of measurements of gene i k = the state of the gene ( as “on” or “off”) θ i = set of parameters of the Gaussian model... legends for mathematical equations

12 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

13 truth is correspondence to reality

14 simple representations can be true

15 a network diagram can be a veridical representation of reality

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17 maps may be correct by reflecting topology, rather than geometry

18 a labeled image can be a more useful veridical representation of reality an image can be a veridical representation of reality

19 an image labelled with computationally tractable labels can be an even more useful veridical representation of reality

20 annotations using common ontologies can yield integration of image data

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

22 two kinds of annotations

23 names of types

24 names of instances

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

26 For ontologies it is generalizations that are important = ontologies are about types, kinds

27 Ontology types Instances

28 Ontology = A Representation of types

29 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

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

31 measurement units are created types

32 Inventory vs. Catalog Two kinds of representational artifact Roughly: Databases represent instances Ontologies represent types

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

34 Catalog vs. inventory

35 Catalog of types/Types

36 siamese mammal cat organism object types animal frog instances

37 Ontologies are here

38 or here

39 ontologies represent general structures in reality (leg)

40 Ontologies do not represent concepts in people’s heads

41 They represent types in reality

42 which provide the benchmark for integration

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

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

45 what are the kinds of entity?

46 First basic distinction universal vs. instance (science text vs. diary) (human being vs. Tom Cruise)

47 Ontology Universals Instances

48 Ontology = A Representation of Universals

49 Ontology = A representation of universals Each node of an ontology consists of: preferred term (aka term) term identifier (TUI, aka CUI) synonyms definition, glosses, comments

50 An ontology is a representation of universals We learn about universals 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

siamese mammal cat organism substance universals animal frog instances

52 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

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

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

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

56 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

57 Analogue representations no representational units, no ‘atoms’

58 Periodic Table The Periodic Table

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

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

61 Problem: roles fist patient FDA-approved drug

62 Administrative ontologies often need to go beyond universals 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

63 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

64 universals vs. their extensions universals {a,b,c,...} collections of particulars

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

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

67 universals vs. classes universals {c,d,e,...} classes

68 universals vs. classes universals ~ defined classes

69 universals vs. classes universals e.g. populations,...

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

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

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

73 universals, classes, concepts universals defined classes ‘concepts’ ?

74 universals < 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

75 (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. universals in reality 2. those relations between these universals which obtain universally (= for all instances) lung is_a anatomical structure lobe of lung part_of lung

Rules for Scientific Ontology How ontology development can be evidence-based 76

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,... ). 77

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 78

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,

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 Feb;3(2):e65. 80

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