1

Introduction to Ontology:Terminology

Barry Smith

http://ontology.buffalo.edu/smith

with thanks to

Werner Ceusters, Waclaw Kusnierczyk, Daniel Schober

2

Problem of ensuring sensible cooperation in a massively interdisciplinary community

concepttypeinstancemodelrepresentationdata

3

What do these mean?

‘conceptual data model’

‘semantic knowledge model’

‘reference information model’

‘an ontology is a specification of a conceptualization’

4

natural language labels

to make the data cognitively accessible to human beings

and algorithmically tractable

5

ontologies are legends for data

6

computationally tractable legends

help human beings find things in very large complex representations of reality

7

Glue-ability / integrationrests 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

maps may be correct by reflecting topology, rather than geometry

9

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

10

two kinds of annotations

11

names of types

12

names of instances

13

First basic distinction

type vs. instance

(science text vs. diary)

(human being vs. Tom Cruise)

14

For ontologies

it is generalizations that are important = ontologies are

about types, kinds, universals

15

Ontology types Instances

16

Ontology = A Representation of types

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

There are created types

bicyclesteering wheelaspirinFord Pinto

we learn about these by looking at manufacturers’ catalogues

19

measurement units are created types

20

Inventory vs. CatalogTwo kinds of representational

artifact

Very roughly:

Databases represent instances

Ontologies represent types

21

A 515287 DC3300 Dust Collector Fan

B 521683 Gilmer Belt

C 521682 Motor Drive Belt

Catalog vs. inventory

22

Catalog vs. inventory

23

Catalog of types/Types

24

siamese

mammal

cat

organism

objecttypes

animal

frog

instances

25

Ontologies are here

26

or here

27

ontologies represent general structures in reality (leg)

28

Ontologies do not represent concepts in people’s heads

29

They represent types in reality

30

which provide the benchmark for integration

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

what are the kinds of entity?

33

First basic distinction

type vs. instance

(science text vs. diary)

(human being vs. Tom Cruise)

34

Ontology Types Instances

35

Ontology = A Representation of types

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

Representation =def

an image, idea, map, picture, name or description ... of some entity or entities.

38

Ontologies are representational artifacts

comparable to science textsand subject to the same sorts of constraints (including need

for update)

39

Representational units =def

terms, icons, alphanumeric identifiers ... which refer, or are intended to refer, to entities

and which are minimal (atoms)

40

Composite representation =defrepresentation

(1) built out of representational units

which

(2) form a structure that mirrors, or is intended to mirror, the entities in some domain

41

Analogue representations

no representational units, no ‘atoms’

42

Periodic Table

The Periodic Table

43

Language has the power to create general terms

which go beyond the domain of types studied by science and documented in catalogs

44

Problem: fiat demarcations

male over 30 years of age with family history of diabetes

abnormal curvature of spine

participant in trial #2030

45

Problem: roles

fist

patient

FDA-approved drug

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

Class =defa 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

types vs. their extensions

types

{a,b,c,...} collections of particulars

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

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

types vs. classes

types

{c,d,e,...} classes

52

types vs. classes

types

~ defined classes

53

types vs. classes

types

e.g. populations, ...

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

OWL is a good representation of defined classes

• sibling of Finnish spy

• member of Abba aged > 50 years

• pizza with > 4 different toppings

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

types, classes, concepts

types

defined classes

‘concepts’ ?

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

(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

Rules for Scientific Ontology

How ontology development can be evidence-based

60

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

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

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

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

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)

OWL and OBO

Description Logic

Linear representation

First Order Logic (SUMO, DOLCE)

BFO (Basic Formal Ontology)

66