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Ontology and the Future of Biomedical Research

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Title: Ontology and the Future of Biomedical Research


1
Ontology and the Future of Biomedical Research
  • Barry Smith
  • http//ifomis.org

2
Institute for Formal Ontology and Medical
Information Science
  • Saarland University

3
From chromosome to disease
4
  • Problem
  • how to reason with data deriving from different
    sources, each of which uses its own system of
    classification ?

5
Solution
Ontology !
6
Examples of current needs for ontologies in
biomedicine
  • to enforce semantic consistency within a database
  • to enable data sharing and re-use
  • to enable data integration (bridging across data
    at multiple granularities)
  • to allow querying

7
What is needed
  • strong general purpose classification
    hierarchies created by domain specialists
  • clear, rigorous definitions
  • thoroughly tested in real use cases
  • updated in light of scientific advance

8
The actuality (too often)
  • myriad special purpose light ontologies,
    prepared by ontology engineers and deposited in
    internet repositories or registries

9
ontologies for agent
10
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11
General trend
  • on the part of NIH, FDA and other bodies to
    consolidate ontology-based standards for the
    communication and processing of biomedical data.

12
Responses to this trend
  • Old UMLS (Unified Medical Language System)
    rooted in the faithfulness to the ways language
    is used by different medical communities

13
U M L S
14
U M L S
  • congenital absent nipple is_a nipple
  • cancer documentation is_a cancer
  • disease prevention is_a disease
  • repair and maintenance of wheelchair is_a
    disease
  • water is_a nursing phenomenon
  • part-whole def. a nursing phenomenon with
    topology part-whole

15
MeSH
  • MeSH Descriptors Index Medicus Descriptor
    Anthropology, Education, Sociology and Social
    Phenomena (MeSH Category) Social
    Sciences
  • Political Systems National
    Socialism

16
MeSH
  • National Socialism is_a Political Systems
  • National Socialism is_a Anthropology ...
  • National Socialism is_a Social Sciences
  • National Socialism is_a MeSH Descriptors

17
  • New Semantic Web deposits
  • Pet Profile Ontology
  • Review Vocabulary
  • Band Description Vocabulary
  • Musical Baton Vocabulary
  • MusicBrainz Metadata Vocabulary
  • Kissology

18
http//www.w3.org/
  • Beer Ontology
  • ? all instances of hops that have ever existed
    are necessarily ingredients of beer.

19
OWL-based ontologies
  • some nice computational resources,
  • but low expressivity
  • and few genuinely scientific demonstration cases

20
OWLs syntactic regimentation is not enough to
ensure high-quality ontologies
  • the use of a common syntax and logical
    machinery and the careful separating out of
    ontologies into namespaces does not solve the
    problem of ontology integration

21
Both UMLS- and OWL-type responses involve ad hoc
creation of new terminologies by each
communityMany of these terminologies remain as
torsos, gather dust, poison the wells, ...

22
  • How to do better?
  • How to create the conditions for a step-by-step
    evolution towards high quality ontologies in the
    biomedical domain
  • which will serve as stable attractors for
    clinical and biomedical researchers in the future?

23
A basic distinction
  • type vs. instance
  • science text vs. clinical document
  • dog vs. Fido

24
Instances are not represented in an ontology
built for scientific purposes
  • It is the generalizations that are important
  • (but instances must still be taken into account)

25
Catalog vs. inventory
A 515287 DC3300 Dust Collector Fan
B 521683 Gilmer Belt
C 521682 Motor Drive Belt

26
Ontology Types Instances




27
Ontology A Representation of Types




28
Ontology A Representation of Types
  • Each node of an ontology consists of
  • preferred term (aka term)
  • term identifier (TUI, aka CUI)
  • synonyms
  • definition, glosses, comments

29
Each term in an ontology represents exactly one
type
  • hence ontology terms should be singular nouns
  • National Socialism is_a Political Systems

30
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 which describe not what
    is particular in reality but rather what is
    general
  • Ontologies need to exploit the evolutionary path
    to convergence created by science

31
High quality shared ontologies build communities
  • NIH, FDA trend to consolidate ontology-based
    standards for the communication and processing of
    biomedical data.
  • caBIG / NECTAR / BIRN / BRIDG ...

32
  • http//obo.sourceforge.net

33
http//www.geneontology.org/
34
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35
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36
The Methodology of Annotations
  • GO employs scientific curators, who use
    experimental observations reported in the
    biomedical literature to link gene products with
    GO terms in annotations.
  • This gene product exercises this function, in
    this part of the cell, leading to these
    biological processes

37
The Methodology of Annotations
  • This process of annotating literature leads to
    improvements and extensions of the ontology,
    which in turn leads to better annotations
  • This institutes a virtuous cycle of improvement
    in the quality and reach of both future
    annotations and the ontology itself.
  • Annotations ontology taken together yield a
    slowly growing computer-interpretable map of
    biological reality.

38
The OBO Foundry
39
  • A subset of OBO ontologies, whose developers
    have agreed in advance to accept a common set of
    principles designed to ensure
  • intelligibility to biologists (curators,
    annotators, users)
  • formal robustness
  • stability
  • compatibility
  • interoperability
  • support for logic-based reasoning

40
  • Custodians
  • Michael Ashburner (Cambridge)
  • Suzanna Lewis (Berkeley)
  • Barry Smith (Buffalo/Saarbrücken)

41
A collaborative experiment
  • participants have agreed in advance to a growing
    set of principles specifying best practices in
    ontology development
  • designed to guarantee interoperability of
    ontologies from the very start

42
  • The developers of each ontology commit to its
    maintenance in light of scientific advance, and
    to soliciting community feedback for its
    improvement.
  • They commit to working with other Foundry
    members to ensure that, for any particular
    domain, there is community convergence on a
    single reference ontology.

43
  • Initial Candidate Members of the OBO Foundry
  • GO Gene Ontology
  • CL Cell Ontology
  • SO Sequence Ontology
  • ChEBI Chemical Ontology
  • PATO Phenotype Ontology
  • FuGO Functional Genomics Investigation Ontology
  • FMA Foundational Model of Anatomy
  • RO Relation Ontology 

44
  • Under development
  • Disease Ontology
  • NCI Thesaurus
  • Mammalian Phenotype Ontology
  • OBO-UBO / Ontology of Biomedical Reality
  • Organism (Species) Ontology
  • Plant Trait Ontology
  • Protein Ontology
  • RnaO RNA Ontology

45
  • Considered for development
  • Environment Ontology
  • Behavior Ontology
  • Biomedical Image Ontology
  • Clinical Trial Ontology

46
CRITERIA
The OBO Foundry
The ontology is open and available to be used by
all. The developers of the ontology agree in
advance to collaborate with developers of other
OBO Foundry ontology where domains overlap. The
ontology is in, or can be instantiated in, a
common formal language.
47
CRITERIA
  • The ontology possesses a unique identifier space
    within OBO.
  • The ontology provider has procedures for
    identifying distinct successive versions.
  • The ontology includes textual definitions for
    all terms.

48
CRITERIA
  • The ontology has a clearly specified and clearly
    delineated content.
  • The ontology is well-documented.
  • The ontology has a plurality of independent
    users.

49
CRITERIA
  • The ontology uses relations which are
    unambiguously defined following the pattern of
    definitions laid down in the OBO Relation
    Ontology.
  • Genome Biology 2005, 6R46

50
CRITERIA
The OBO Foundry
  • Further criteria will be added over time in
    order to bring about a gradual improvement in the
    quality of the ontologies in the Foundry

51
A reference ontology
  • is analogous to a scientific theory it seeks to
    optimize representational adequacy to its subject
    matter to the maximal degree that is compatible
    with the constraints of computational usefulness.

52
An application ontology
  • is comparable to an engineering artifact such as
    a software tool. It is constructed for a specific
    practical purpose.
  • Examples
  • National Cancer Institute Thesaurus
  • FuGO Functional Genomics Investigation
    Ontology

53
Reference Ontology vs. Application Ontology
  • Currently, application ontologies are often
    built afresh for each new task commonly
    introducing not only idiosyncrasies of format or
    logic, but also simplifications or distortions of
    their subject-matters.
  • To solve this problem application ontology
    development should take place always against the
    background of a formally robust reference
    ontology framework

54
Advantages of the methodology of shared
coherently defined ontologies
  • promotes quality assurance (better coding)
  • guarantees automatic reasoning across ontologies
    and across data at different granularities
  • yields direct connection to temporally indexed
    instance data

55
Advantages of the methodology of shared
coherently defined ontologies
  • We know that high-quality ontologies can help in
    creating better mappings e.g. between human and
    model organism phenotypes
  • S Zhang, O Bodenreider, Alignment of Multiple
    Ontologies of Anatomy Deriving Indirect Mappings
    from Direct Mappings to a Reference Ontology,
    AMIA 2005

56
Advantages of the methodology of shared
coherently defined ontologies
  • once the interoperable gold standard reference
    ontologies are there, it will make sense to
    reformulate parts of existing incompatible
    terminologies (e.g. in UMLS) in terms of the
    standard ontologies in order to achieve greater
    domain coverage and alignment of different but
    veridical views. Thus not everything that was
    done in the past turns out to be a waste.

57
  • Goal
  • to create a family of gold standard reference
    ontologies upon which terminologies developed for
    specific applications can draw

58
  • Goal
  • to introduce the scientific method into ontology
    development
  • all Foundry ontologies must be constantly updated
    in light of scientific advance
  • all Foundry ontology developers must work with
    all other Foundry ontology developers in a spirit
    of scientific collaboration

59
  • Goal
  • to replace the current policy of ad hoc
    creation of new database schemas by each clinical
    research group by providing reference ontologies
    in terms of which database schemas can be defined

60
  • Goal
  • to introduce some of the features of scientific
    peer review into biomedical ontology development

61
  • Goal
  • to create controlled vocabularies for use by
    clinical trial banks, clinical guidelines bodies,
    scientific journals, ...

62
  • Goal
  • to create controlled vocabularies for use by
    clinical trial banks, clinical guidelines bodies,
    scientific journals, ...

63
  • Goal
  • to create an evolving map-like representation of
    the entire domain of biological reality

64
GOs three ontologies
biological process
molecular function
cellular component
65
organism-level physiology
molecular process
cellular physiology
molecular function (GO)
cell (types)
species
ChEBI, Sequence, RNA ...
cellular anatomy
anatomy (fly, fish, human...)
66
organism-level physiology
cellular physiology
molecular process
normal (functionings)
molecular function (GO)
cell (types)
species
ChEBI, Sequence, RNA ...
cellular anatomy
anatomy (fly, fish, human...)
67
pathophysiology (disease)
pathological (malfunctionings)
pathoanatomy (fly, fish, human ...)
68
pathophysiology (disease)
organism-level physiology
cellular physiology
molecular process
molecular function (GO)
pathoanatomy (fly, fish, human ...)
cell (types)
species
ChEBI, Sequence, RNA ...
cellular anatomy (GO)
anatomy (fly, fish, human...)
69
pathophysiology (disease)
organism-level physiology
cellular physiology
molecular process
molecular function (GO)
phenotype
pathoanatomy (fly, fish, human ...)
cell (types)
species
ChEBI, Sequence, RNA ...
cellular anatomy
anatomy (fly, fish, human...)
70
pathophysiology (disease)
organism-level physiology
cellular physiology
molecular process
molecular function (GO)
phenotype
pathoanatomy (fly, fish, human ...)
cell (types)
species
ChEBI, Sequence, RNA ...
cellular anatomy
anatomy (fly, fish, human...)
investigation (FuGO)
71
Ende
72
First step
  • Alignment of OBO Foundry ontologies through a
    common system of formally defined relations in
    the OBO Relation Ontology
  • See Relations in Biomedical Ontologies, Genome
    Biology Apr. 2005

73
  • Judith Blake
  • The use of bio-ontologies ensures consistency
    of data curation, supports extensive data
    integration, and enables robust exchange of
    information between heterogeneous informatics
    systems. ..
  • ontologies formally define relationships
    between the concepts.

74
"Gene Ontology Tool for the Unification of
Biology"
  • an ontology "comprises a set of well-defined
    terms with well-defined relationships"
  • (Ashburner et al., 2000, p. 27)

75
is_a (sensu UMLS)
  • A is_a B def
  • A is narrower in meaning than B
  • grows out of the heritage of dictionaries
  • (which ignore the basic distinction between types
    and instances)

76
is_a
  • congenital absent nipple is_a nipple
  • cancer documentation is_a cancer
  • disease prevention is_a disease
  • Nazism is_a social science

77
is_a (sensu logic)
  • A is_a B def
  • For all x, if x instance_of A then x instance_of
    B
  • cell division is_a biological process
  • adult is_a child ???

78
Two kinds of entities
  • occurrents (processes, events, happenings)
  • cell division, ovulation, death
  • continuants (objects, qualities, ...)
  • cell, ovum, organism, temperature of organism,
    ...

79
is_a (for occurrents)
  • A is_a B def
  • For all x, if x instance_of A then x instance_of
    B
  • cell division is_a biological process

80
is_a (for continuants)
  • A is_a B def
  • For all x, t if x instance_of A at t then x
    instance_of B at t
  • abnormal cell is_a cell
  • adult human is_a human
  • but not adult is_a child

81
Part_of as a relation between types is more
problematic than is standardly supposed
  • heart part_of human being ?
  • human heart part_of human being ?
  • human being has_part human testis ?
  • human testis part_of human being ?

82
two kinds of parthood
  • between instances
  • Marys heart part_of Mary
  • this nucleus part_of this cell
  • between types
  • human heart part_of human
  • cell nucleus part_of cell

83
Definition of part_of as a relation between types
  • A part_of B Def all instances of A are
    instance-level parts of some instance of B
  • ALLSOME STRUCTURE

84
part_of (for occurrents)
  • A part_of B Def
  • For all x, if x instance_of A then there is some
    y, y instance_of B and x part_of y
  • where part_of is the instance-level part
    relation

85
part_of (for continuants)
  • A part_of B def.
  • For all x, t if x instance_of A at t then there
    is some y, y instance_of B at t and x part_of y
  • where part_of is the instance-level part
    relation
  • ALL-SOME STRUCTURE

86
How to use the OBO Relation Ontology
  • Ontologies are representations of types and of
    the relations between types
  • The definitions of these relations involve
    reference to times and instances, but these
    references are washed out when we get to the
    assertions (edges) in the ontology
  • But curators should still be aware of the
    underlying definitions when formulating such
    assertions

87
part_of (for occurrents)
  • A part_of B Def
  • For all x, if x instance_of A then there is some
    y, y instance_of B and x part_of y
  • where part_of is the instance-level part
    relation

88
A part_of B, B part_of C ...
  • The all-some structure of such definitions allows
  • cascading of inferences (true path rule)
  • (i) within ontologies
  • (ii) between ontologies
  • (iii) between ontologies and repositories of
    instance-data

89
Strengthened true path rule
  • Whichever A you choose, the instance of B of
    which it is a part will be included in some C,
    which will include as part also the A with which
    you began
  • The same principle applies to the other relations
    in the OBO-RO
  • located_at, transformation_of, derived_from,
    adjacent_to, etc.

90
Kinds of relations
  • Between types
  • is_a, part_of, ...
  • Between an instance and a type
  • this explosion instance_of the type explosion
  • Between instances
  • Marys heart part_of Mary

91
In every ontology
  • some terms and some relations are primitive
    they cannot be defined (on pain of infinite
    regress)
  • Examples of primitive relations
  • identity
  • instantiation
  • (instance-level) part_of
  • (instance-level) continuous_with

92
Fiat and bona fide boundaries
93
Continuity Attachment Adjacency
94
everything here is an independent continuant
95
structures vs. formations bona fide vs. fiat
boundaries
96
Modes of Connection
  • The body is a highly connected entity.
  • Exceptions cells floating free in blood.

97
Modes of Connection
  • Modes of connection
  • attached_to (muscle to bone)
  • synapsed_with (nerve to nerve, nerve to muscle)
  • continuous_with ( share a fiat boundary)

98
articular eminence
articular (glenoid)fossa
ANTERIOR
Attachment, location, containment
99
Containment involves relation to a hole or cavity
1 cavity 2 tunnel, conduit (artery) 3 mouth a
snails shell
100
Fiat vs. Bona Fide Boundaries
101
Double Hole Structure
Retainer
(a boundary of some
surrounding structure)

Medium
(filling the environing hole)

Tenant
(occupying the central hole)
102
head of condyle
fossa
fiat boundary
neck of condyle
THE TEMPOROMANDIBULAR JOINT
103
continuous_with(a relation between instances
which share a fiat boundary)
  • is always symmetric
  • if x continuous_with y , then y continuous_with
    x

104
continuous_with(relation between types)
  • A continuous_with B Def.
  • for all x, if x instance-of A then there is some
    y such that y instance_of B and x
    continuous_with y

105
continuous_with is not always symmetric
  • Consider lymph node and lymphatic vessel
  • Each lymph node is continuous with some
    lymphatic vessel, but there are lymphatic vessels
    (e.g. lymphs and lymphatic trunks) which are not
    continuous with any lymph nodes

106
Adjacent_toas a relation between types is not
symmetric
  • Consider
  • seminal vesicle adjacent_to urinary bladder
  • Not urinary bladder adjacent_to seminal vesicle

107
  • instance level
  • this nucleus is adjacent to this cytoplasm
  • implies
  • this cytoplasm is adjacent to this nucleus
  • type level
  • nucleus adjacent_to cytoplasm
  • Not cytoplasm adjacent_to nucleus

108
Applications
  • Expectations of symmetry e.g. for protein-protein
    interactions may hold only at the instance level
  • if A interacts with B, it does not follow that B
    interacts with A
  • if A is expressed simultaneously with B, it does
    not follow that B is expressed simultaneously
    with A

109
transformation_of
110
transformation_of
  • A transformation_of B Def.
  • Every instance of A was at some earlier time an
    instance of B
  • adult transformation_of child

111
tumor development
112
derives_from
C1 c1 at t1
C c at t
time
C' c' at t
ovum
zygote derives_from
sperm
113
two continuants fuse to form a new continuant
C1 c1 at t1
C c at t
C' c' at t
fusion
114
one initial continuant is replaced by two
successor continuants
C1 c1 at t1
C c at t
C2 c1 at t1
fission
115
one continuant detaches itself from an initial
continuant, which itself continues to exist
C c at t
c at t1
C1 c1 at t
budding
116
one continuant absorbs a second continuant while
itself continuing to exist
c at t1
C c at t
C' c' at t
capture
117
A suite of defined relations between types
Foundational is_apart_of
Spatial located_incontained_inadjacent_to
Temporal transformation_ofderives_frompreceded_by
Participation has_participanthas_agent
118
To be added to the Relation Ontology
  • lacks (between an instance and a type, e.g. this
    fly lacks wings)
  • dependent_on (between a dependent entity and its
    carrier or bearer)
  • quality_of (between a dependent and an
    independent continuant)
  • functioning_of (between a process and an
    independent continuant)

119
Low Hanging Fruit
  • Ontologies should include only those relational
    assertions which hold universally ( have the
    ALL-SOME form)
  • Often, order will matter here
  • We can include
  • adult transformation_of child
  • but not
  • child transforms_into adult

120
The Gene Ontology
121
GOs three ontologies
molecular functions
biological processes
cellular components
122
When a gene is identified
  • three types of questions need to be addressed
  • 1. Where is it located in the cell?
  • 2. What functions does it have on the molecular
    level?
  • 3. To what biological processes do these
    functions contribute?

123
Three granularities
  • Cellular (for components)
  • Molecular (for functions)
  • Organ organism (for processes)

124
GO has cells
  • but it does not include terms for molecules or
    organisms within any of its three ontologies
  • except e.g. GO0018995 host
  • Def. Any organism in which another organism
    spends part or all of its life cycle

125
Are the relations between functions and processes
a matter of granularity?
  • Molecular activities are the building blocks of
    biological processes ?
  • But they are not allowed to be represented in GO
    as parts of biological processes

126
GOs three ontologies
biological processes
molecular functions
cellular components
127
  • What does function mean?
  • an entity has a biological function if and only
    if it is part of an organism and has a
    disposition to act reliably in such a way as to
    contribute to the organisms survival
  • the function is this disposition

128
Improved version
  • an entity has a biological function if and only
    if it is part of an organism and has a
    disposition to act reliably in such a way as to
    contribute to the organisms realization of the
    canonical life plan for an organism of that type

129
This canonical life plan might include
  • canonical embryological development
  • canonical growth
  • canonical reproduction
  • canonical aging
  • canonical death

130
The function of the heart is to pump blood
  • Not every activity (process) in an organism is
    the exercise of a function there are
  • mal functionings
  • side-effects (heart beating)
  • accidents (external interference)
  • background stochastic activity

131
Kidney
132
Nephron
133
Functional Segments
134
Functions
135
Functions
  • This is a screwdriver
  • This is a good screwdriver
  • This is a broken screwdriver
  • This is a heart
  • This is a healthy heart
  • This is an unhealthy heart

136
Functions are associated with certain
characteristic process shapes
  • Screwdriver rotates and simultaneously moves
    forward simultaneously transferring torque from
    hand and arm to screw
  • Heart performs a contracting movement inwards
    and an expanding movement outwards

137
Not functioning at all
  • leads to death, modulo
  • internal factors
  • plasticity
  • redundancy (2 kidneys)
  • criticality of the system involved
  • external factors
  • prosthesis (dialysis machines, oxygen tent)
  • special environments
  • assistance from other organisms

138
What clinical medicine is for
  • to eliminate malfunctioning by fixing broken
    body parts
  • (or to prevent the appearance of malfunctioning
    by intervening e.g. at the molecular level)

139
Hypothesis there are no bad functions
  • It is not the function of an oncogene to cause
    cancer
  • Oncogenes were in every case proto-oncogenes
    with functions of their own
  • They become oncogenes because of bad
    (non-prototypical) environments

140
Is there an exception for molecular functions?
  • Does this apply only to functions on biological
    levels of granularity
  • ( levels of granularity coarser than the
    molecule) ?
  • If pathology is the deviation from (normal)
    functioning, does it make sense to talk of a
    pathological molecule?
  • (Pathologically functioning molecule vs.
    pathologically structured molecule)

141
Is there an exception for molecular functions?
  • A molecular function is a propensity of a gene
    product instance to perform actions on the
    molecular level of granularity.
  • Hypothesis 1 these actions must be reliably
    such as to contribute to biological processes.
  • Hypothesis 2 these actions must be reliably
    such as to contribute to the organisms
    realization of the canonical life plan for an
    organism of that type.

142
The Gene Ontology
  • is a canonical ontology it represents only
    what is normal in the realm of molecular
    functioning

143
The GO is a canonical representation
  • The Gene Ontology is a computational
    representation of the ways in which gene products
    normally function in the biological realm
  • Nucl. Acids Res. 2006 34.

144
The FMA is a canonical representation
  • It is a computational representation of types
    and relations between types deduced from the
    qualitative observations of the normal human
    body, which have been refined and sanctioned by
    successive generations of anatomists and
    presented in textbooks and atlases of structural
    anatomy.

145
The importance of pathways (successive causality)
  • Each stage in the history of a disease
    presupposes the earlier stages
  • Therefore need to reason across time, tracking
    the order of events in time, using relations such
    as derives_from, transformation_of ...
  • Need pathway ontologies on every level of
    granularity

146
The importance of granularity (simultaneous
causality)
  • Networks are continuants
  • At any given time there are networks existing in
    the organism at different levels of granularity
  • Changes in one cause simultaneous changes in all
    the others
  • (Compare Boyles law a rise in temperature
    causes a simultaneous increase in pressure)

147
The Granularity Gulf
  • most existing data-sources are of fixed, single
    granularity
  • many (all?) clinical phenomena cross
    granularities
  • Therefore need to reason across time, tracking
    the order of events in time

148
Good ontologies require
  • consistent use of terms, supported by logically
    coherent (non-circular) definitions, in
    equivalent human-readable and computable formats
  • coherent shared treatment of relations to allow
    cascading inference both within and between
    ontologies

149
Three fundamental dichotomies
  • continuants vs. occurrents
  • dependent vs. independent
  • types vs. instances

150
ONTOLOGIES AREREPRESENTATIONS OF TYPESaka
kinds, universals, categories, species, genera,
...
151
  • Continuants (aka endurants)
  • have continuous existence in time
  • preserve their identity through change
  • exist in toto whenever they exist at all
  • Occurrents (aka processes)
  • have temporal parts
  • unfold themselves in successive phases
  • exist only in their phases

152
You are a continuant
  • Your life is an occurrent
  • You are 3-dimensional
  • Your life is 4-dimensional

153
Dependent entities
  • require independent continuants as their bearers
  • There is no run without a runner
  • There is no grin without a cat

154
Dependent vs. independent continuants
  • Independent continuants (organisms, cells,
    molecules, environments)
  • Dependent continuants (qualities, shapes, roles,
    propensities, functions)

155
All occurrents are dependent entities
  • They are dependent on those independent
    continuants which are their participants (agents,
    patients, media ...)

156
Top-Level Ontology
Continuant
Occurrent (always dependent on one or more
independent continuants)
Independent Continuant
Dependent Continuant
157
A representation of top-level types
Continuant
Occurrent
biological process
Independent Continuant
Dependent Continuant
cell component
molecular function
158
Top-Level Ontology
Continuant
Occurrent
Independent Continuant
Dependent Continuant
Side-Effect, Stochastic Process, ...
Functioning
Function
159
Top-Level Ontology
Continuant
Occurrent
Independent Continuant
Dependent Continuant
Functioning
Side-Effect, Stochastic Process, ...
Function
160
Top-Level Ontology
instances (in space and time)
161
Smith B, Ceusters W, Kumar A, Rosse C. On
Carcinomas and Other Pathological Entities, Comp
Functional Genomics, Apr. 2006
162
everything here is an independent continuant
163
Functions, etc.Some dependent continuants are
realizable
  • expression of a gene
  • application of a therapy
  • course of a disease
  • execution of an algorithm
  • realization of a protocol

164
Functions vs Functionings
  • the function of your heart to pump blood in
    your body
  • this function is realized in processes of
    pumping blood
  • not all functions are realized (consider the
    function of this sperm ...)

165
Concepts
  • Biomedical ontology integration will never be
    achieved through integration of meanings or
    concepts
  • The problem is precisely that different user
    communities use different concepts
  • Concepts are in your head and will change as your
    understanding changes

166
Concepts
  • Ontologies represent types not concepts,
    meanings, ideas ...
  • Types exist, with their instances, in objective
    reality
  • including types of image, of imaging process,
    of brain region, of clinical procedure, etc.

167
Rules on types
  • Dont confuse types with words
  • Dont confuse types with concepts
  • Dont confuse types with ways of getting to know
    types
  • Dont confuse types with ways of talking about
    types
  • Dont confuses types with data about types

168
Some other simple rules for high quality
ontologies
169
Univocity
  • Terms should have the same meanings on every
    occasion of use.
  • They should refer to the same kinds of entities
    in reality
  • Basic ontological relations such as is_a and
    part_of should be used in the same way by all
    ontologies

170
Positivity
  • Complements of types are not themselves types.
  • Hence terms such as
  • non-mammal
  • non-membrane
  • other metalworker in New Zealand
  • do not designate types in reality

171
Ontology of types ? logic of terms
  • There are no conjunctive and disjunctive types
  • anatomic structure, system, or substance
  • musculoskeletal and connective tissue disorder
  • rheumatism, excluding the back

172
Objectivity
  • Which types exist in reality is not a function of
    our knowledge.
  • Terms such as
  • unknown
  • unclassified
  • unlocalized
  • arthropathies not otherwise specified
  • do not designate types in reality.

173
Keep Epistemology Separate from Ontology
  • If you want to say that
  • We do not know where As are located
  • do not invent a new class of
  • As with unknown locations
  • (A well-constructed ontology should grow
    linearly it should not need to delete classes or
    relations because of increases in knowledge)

174
Syntactic SeparatenessDo not confuse sentences
with terms
  • If you want to say
  • I surmise that this is a case of pneumonia
  • do not invent a new class of surmised pneumonias

175
Single Inheritance
  • No kind in a classificatory hierarchy should
    have more than one is_a parent on the immediate
    higher level

176
Multiple Inheritance
  • thing

car
blue thing
is_a
is_a
blue car
177
Multiple Inheritance
  • is a source of errors
  • encourages laziness
  • serves as obstacle to integration with
    neighboring ontologies
  • hampers use of Aristotelian methodology for
    defining terms

178
Multiple Inheritance
  • thing

blue thing
car
is_a1
is_a2
blue car
179
is_a Overloading
  • The success of ontology alignment demands that
    ontological relations (is_a, part_of, ...) have
    the same meanings in the different ontologies to
    be aligned.

180
Example is_a is pressed into service by the GO
to express location
  • is-located-at and similar relations are
    expressed by creating special compound terms
    using
  • site of
  • within
  • in
  • extrinsic to
  • yielding associated errors

181
e.g. errors with within
  • lytic vacuole within a protein storage vacuole
  • lytic vacuole within a protein storage vacuole
    is-a protein storage vacuole
  • Compare
  • embryo within a uterus is-a uterus

182
similar problems with part_of
  • extrinsic to membrane part_of membrane

183
Compositionality
  • The meanings of compound terms should be
    determined
  • 1. by the meanings of component terms
  • together with
  • 2. the rules governing syntax

184
Why do we need rules/standards for good ontology?
  • Ontologies must be intelligible both to humans
    (for annotation and curation) and to machines
    (for reasoning and error-checking) the lack of
    rules for classification leads to human error and
    blocks automatic reasoning and error-checking
  • Intuitive rules facilitate training of curators
    and annotators
  • Common rules allow alignment with other
    ontologies

185
When we annotate the record of an experiment
  • we use terms representing types to capture what
    we learn about
  • this experiment (instance), performed here and
    now, in this laboratory
  • the instances experimented upon
  • These instances are typical they are
    representatives of types
  • of experiment (described in FuGO)
  • of gene product molecules, molecular functions,
    cellular components, biological processes
    (described in GO)

186
Experimental records
  • document a variety of instances (particular
    real-world examples or cases), ranging from
    instances of gene products (including individual
    molecules) to instances of biochemical processes,
    molecular functions, and cellular locations

187
Experimental records
  • provide evidence that gene products of given
    types have molecular functions of given types by
    documenting occurrences in the real world that
    involve corresponding instances of functioning.
  • They document the existence of real-world
    molecules that have the potential to execute
    (carry out, realize, perform) the types of
    molecular functions that are involved in these
    occurrences.

188
Motivation To capture reality
  • Inferences and decisions we make are based upon
    what we know of reality.
  • An ontology is a computable representation of
    biological reality, which is designed to enable a
    computer to reason over the data we collect about
    this reality in (some of) the ways that we do.
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