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Towards a Reference Terminology for Talking about Ontologies and

Related Artifacts

Barry Smith

http://ontology.buffalo.edu/smith

with thanks to

Werner Ceusters, Waclaw Kusnierczyk, Daniel Schober

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Problem of ensuring sensible cooperation in a massively interdisciplinary community

concepttypeinstancemodelrepresentationdata

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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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natural language labels

to make the data cognitively accessible to human beings

and algorithmically tractable

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compare: legends for mapscompare: legends for maps

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ontologies are legends for data

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compare: legends for cartoons

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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

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computationally tractable legends

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

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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......

legends for mathematical equations

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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

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truth is correspondence to reality

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simple representations can be true

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a network diagram can be a veridical representation of reality

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maps may be correct by reflecting topology, rather than geometry

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

an image can be a veridical representation of reality

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an image labelled with computationally tractable labels can be an even more useful veridicalrepresentation of reality

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annotations using common ontologies can yield integration of image data

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

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two kinds of annotations

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names of types

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names of instances

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First basic distinction

type vs. instance

(science text vs. diary)

(human being vs. Tom Cruise)

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For ontologies

it is generalizations that are important = ontologies are

about types, kinds

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Ontology types Instances

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Ontology = A Representation of types

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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

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There are created types

bicyclesteering wheelaspirinFord Pinto

we learn about these by looking at manufacturers’ catalogues

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measurement units are created types

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Inventory vs. CatalogTwo kinds of representational

artifact

Roughly:

Databases represent instances

Ontologies represent types

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A 515287 DC3300 Dust Collector Fan

B 521683 Gilmer Belt

C 521682 Motor Drive Belt

Catalog vs. inventory

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Catalog vs. inventory

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Catalog of types/Types

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siamese

mammal

cat

organism

objecttypes

animal

frog

instances

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Ontologies are here

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or here

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ontologies represent general structures in reality (leg)

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Ontologies do not represent concepts in people’s heads

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They represent types in reality

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which provide the benchmark for integration

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

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Entity =def

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

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what are the kinds of entity?

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First basic distinction

universal vs. instance

(science text vs. diary)

(human being vs. Tom Cruise)

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Ontology Universals Instances

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Ontology = A Representation of Universals

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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

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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

substanceuniversals

animal

frog

instances

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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

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Representation =def

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

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Ontologies are representational artifacts

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

for update)

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Representational units =def

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

and which are minimal (atoms)

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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

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Analogue representations

no representational units, no ‘atoms’

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Periodic Table

The Periodic Table

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Language has the power to create general terms

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

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Problem: fiat demarcations

male over 30 years of age with family history of diabetes

abnormal curvature of spine

participant in trial #2030

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Problem: roles

fist

patient

FDA-approved drug

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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

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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

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universals vs. their extensions

universals

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

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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)

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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

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universals vs. classes

universals

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

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universals vs. classes

universals

~ defined classes

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universals vs. classes

universals

e.g. populations, ...

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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

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OWL is a good representation of defined classes

• sibling of Finnish spy

• member of Abba aged > 50 years

• pizza with > 4 different toppings

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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

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universals, classes, concepts

universals

defined classes

‘concepts’ ?

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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

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(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

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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, ... ).

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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

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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

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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.

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