This document discusses ontologies and their uses. It provides a brief history of the Semantic Web and efforts to integrate online data using technologies like RDF and OWL. It notes that while these efforts led to some successes, they also resulted in new "semantic silos" as multiple independent ontologies were created. The document advocates for a more coordinated approach to ontology development to minimize the need for mappings between ontologies and discusses successes of ontologies like Gene Ontology and the Open Biomedical Ontologies Foundry principles in addressing these issues.

1. What is an Ontology and What
is it Useful For?
Barry Smith
http://ontology.buffalo.edu/smith
1

2. A brief history of the Semantic Web
• html demonstrated the power of the Web to allow
sharing of information
• can we use semantic technology to create a Web 2.0
which would allow algorithmic reasoning with online
information based on XLM, RDF and above all OWL
(Web Ontology Language)?
• can we use RDF and OWL to break down silos, and
create useful integration of on-line data and
information
2

3. people tried, but the more they were successful,
they more they failed
OWL breaks down data silos via controlled
vocabularies for the description of data
dictionaries
Unfortunately the very success of this approach
led to the creation of multiple, new, semantic
silos – because multiple ontologies are being
created in ad hoc ways
3

4. reasons for this effect
• Tim Berners Lee mentality
– let a million ‘lite ontologies bloom’, and somehow
intelligence will be created
– ‘links’ can mean anything (à la html)
• shrink-wrapped software mentality – you will
not get paid for reusing old and good
ontologies
• requirements-driven software development
• reducing potential secondary uses
4/24

5. Ontology success stories, and some
reasons for failure
•
A fragment of the “Linked Open
Data” in the biomedical domain
5

6. What you get with ‘mappings’
HPO: all phenotypes (excess hair loss, duck
feet)
6

7. What you get with ‘mappings’
HPO: all phenotypes (excess hair loss, duck feet ...)
NCIT: all organisms
7

8. What you get with ‘mappings’
all phenotypes (excess hair loss, duck feet)
all organisms
allose (a form of sugar)
8

9. What you get with ‘mappings’
all phenotypes (excess hair loss, duck feet)
all organisms
allose (a form of sugar)
Acute Lymphoblastic Leukemia (A.L.L.)
9

10. 10

11. Mappings are hard
They are fragile, and expensive to maintain
Need a new authority to maintain, yielding new
risk of forking
The goal should be to minimize the need for
mappings
Invest resources in disjoint ontology modules
which work well together – reduce need for
mappings to minimum possible
11

12. Why should you care?
• you need to create systems for data mining
and text processing which will yield useful
digitally coded output
• if the codes you use are constantly in need of
ad hoc repair huge resources will be wasted
• relevant data will not be found
• serious reasoning will be defeated from the
start
12/24

13. 13

14. 14

15. …
15

16. How to do it right?
• how create an incremental, evolutionary
process, where what is good survives, and what
is bad fails
• where the number of ontologies needing to be
linked is small
• where links are stable
• create a scenario in which people will find it
profitable to reuse ontologies, terminologies and
coding systems which have been tried and tested
16/24

17. Uses of ‘ontology’ in PubMed abstracts
17

18. By far the most successful: GO (Gene Ontology)
18

19. GO provides a controlled system of terms
for use in annotating (describing, tagging)
data
• multi-species, multi-disciplinary, open
source
• contributing to the cumulativity of scientific
results obtained by distinct research
communities
• compare use of kilograms, meters, seconds
in formulating experimental results
19

20. Hierarchical view representing
relations between represented
20
types

21. Anatomical
Anatomical Space
Structure
Organ Cavity Organ
Organ Organ Part
Subdivision Cavity
Serous Sac Serous Sac Organ Organ
Cavity Cavity
Serous Sac Component Subdivision
Tissue
Subdivision
is_a
Pleural Sac
Pleural Sac Pleura(Wall
Pleural Pleura(Wall
Pleural of Sac)
of Sac)
Cavity
of
Cavity
Parietal
Parietal
Pleura
t_
Pleura Visceral
Visceral
Interlobar Pleura
Pleura
Interlobar
r
recess
recess Mediastinal
pa
Mediastinal
Pleura
Pleura Mesothelium
Mesothelium
of Pleura
of Pleura
21
Foundational Model of Anatomy (FMA)

22. US $100 mill. invested in literature and
data curation using GO
over 11 million annotations relating gene
products described in the UniProt, Ensembl
and other databases to terms in the GO
experimental results reported in 52,000
scientific journal articles manually annoted by
expert biologists using GO
22

23. Reasons why GO has been
successful
It is a system for prospective standardization
built with coherent top level but with content
contributed and monitored by domain specialists
Based on community consensus
Updated every night
Clear versioning principles ensure backwards
compatibility; prior annotations do not lose their
value
Initially low-tech to encourage users, with
movement to more powerful formal approaches
(including OWL-DL – though GO community still
recommending caution)
23

24. GO has learned the lessons of
successful cooperation
• Clear documentation
• The terms chosen are already familiar
• Fully open source (allows thorough testing in
manifold combinations with other ontologies)
• Subjected to considerable third-party critique
• Rapid turnaround tracker and help desk
• Usable also for education
• Focus on reality
24

25. Why is the focus on reality
important
Each community, each local data structure, has
its own conceptualization
What shall serve as benchmark for the
integration of the data generated by data
communities?
Answer: Reality, as understood by bench
scientists
Conclusion: Bench scientists have to be
involved in the construction and coordination
of ontologies
25

26. Data structures and ontologies have different
purposes
Information models and ontologies are
at different levels
• The purpose of an information model is:to
specify valid data structures to carry
information
• To constrain the data structures to just
those which a given software system can
process
The purpose of an ontology is to represent
the world
26

27. Data structures and ontologies have
different characteristics
All persons have a sex
However not all data structures about
people have a field for sex
Information structures are intrinsically
closed
We can describe them completely
Ontologies are intrinsically open
We can never describe the real world
completely
27

28. Benefits of GO
Establishing a bridge between the molecular/gene
level and higher order biology – you get nothing
by just looking at genes.
Building up a larger picture of biological systems as
a mosaic of areas studied in depth by one or
other of the model organism databases (but never
all, and not all in any one)
Creating a view to link studies on different
organisms.
28

29. Sample Gene Array Data
29

30. where in the body ?
what kind of
disease process ?
 need for semantic annotation of data
30

31. natural language labels
to make the data cognitively
accessible to human beings
31

32. compare: legends for maps
32

33. ontologies are legends for data
33

34. annotation with Gene Ontology
supports reusability of data
supports search of data by humans
supports reasoning with data by humans and
machines
34

35. GO has been amazingly successful in
overcoming the data balkanization
problem
but it covers only generic biological entities of
three sorts:
– cellular components
– molecular functions
– biological processes
and it does not provide representations of
diseases, symptoms, …
35

36. CONTINUANT OCCURRENT
RELATION
TO TIME
INDEPENDENT DEPENDENT
GRANULARITY
Anatomical
Organism Organ
ORGAN AND Entity
(NCBI Function
ORGANISM (FMA,
Taxonomy) (FMP, CPRO) Phenotypic Biological
CARO) Quality Process
(PaTO) (GO)
CELL AND Cellular Cellular
Cell
CELLULAR Component Function
(CL)
COMPONENT (FMA, GO) (GO)
Molecule
Molecular Function Molecular Process
MOLECULE (ChEBI, SO,
(GO) (GO)
RnaO, PrO)
Original OBO Foundry ontologies
(Gene Ontology in yellow) 36

37. CONTINUANT OCCURRENT
RELATION
TO TIME
INDEPENDENT DEPENDENT
GRANULARITY
Anatomical
Organism Organ
ORGAN AND Entity
(NCBI Function
ORGANISM (FMA,
Taxonomy) (FMP, CPRO) Phenotypic
environments
CARO) Biological
Quality Process
(PaTO) (GO)
are here
CELL AND Cellular Cellular
Cell
CELLULAR Component Function
(CL)
COMPONENT (FMA, GO) (GO)
Molecule
Molecular Function Molecular Process
MOLECULE (ChEBI, SO,
(GO) (GO)
RnaO, PrO)
Environment Ontology
37

38. CONTINUANT OCCURRENT
RELATION
TO TIME
INDEPENDENT DEPENDENT
GRANULARITY
COMPLEX OF Family, Community, Population Population
ORGANISMS Deme, Population Phenotype Process
Anatomical Organ
ORGAN AND Organism Entity Function
ORGANISM (NCBI (FMA, (FMP, CPRO) Phenotypic
Taxonomy) Biological
CARO) Quality
Process
(PaTO)
(GO)
CELL AND Cellular Cellular
Cell
CELLULAR Component Function
(CL)
COMPONENT (FMA, GO) (GO)
Molecule
Molecular Function Molecular Process
MOLECULE (ChEBI, SO,
(GO) (GO)
RnaO, PrO)
http://obofoundry.org 38

39. Ontology success stories, and
some reasons for failure
•
39

40. CONTINUANT OCCURRENT
RELATION
TO TIME
INDEPENDENT DEPENDENT
GRANULARITY
COMPLEX OF Family, Community, Population Population
ORGANISMS Deme, Population Phenotype Process
Anatomical Organ
ORGAN AND Organism Entity Function
ORGANISM (NCBI (FMA, (FMP, CPRO) Phenotypic
Taxonomy) Biological
CARO) Quality
Process
(PaTO)
(GO)
CELL AND Cellular Cellular
Cell
CELLULAR Component Function
(CL)
COMPONENT (FMA, GO) (GO)
Molecule
Molecular Function Molecular Process
MOLECULE (ChEBI, SO,
(GO) (GO)
RnaO, PrO)
http://obofoundry.org 40

41. The OBO Foundry: a step-by-step,
evidence-based approach to expand
the GO
 Developers commit to working to ensure
that, for each domain, there is community
convergence on a single ontology
 and agree in advance to collaborate with
developers of ontologies in adjacent
domains.
http://obofoundry.org 41

42. OBO Foundry Principles
 Common governance (coordinating editors)
 Common training
 Common architecture to overcome Tim
Berners Lee-ism:
• simple shared top level ontology
• shared Relation Ontology:
www.obofoundry.org/ro
42

43. Open Biomedical Ontologies Foundry
Seeks to create high quality, validated terminology
modules across all of the life sciences which will be
• one ontology for each domain, so no need for
mappings
• close to language use of experts
• evidence-based
• incorporate a strategy for motivating potential
developers and users
• revisable as science advances 43

44. A prospective standard
designed to guarantee interoperability of ontologies
from the very start (and to keep out weeds)
initial set of 10 criteria tested in the annotation of
scientific literature
model organism databases
life science experimental results
44

45. ORTHOGONALITY
modularity ensures
• annotations can be additive
• division of labor amongst domain experts
• high value of training in any given module
• lessons learned in one module can benefit
work on other modules
• incentivization of those responsible for
individual modules
45

46. Benefits of coordination
Can profit from lessons learned through mistakes
made by others
Can more easily reuse what is made by others
Can more easily inspect and criticize results of
others’ work
Leads to innovations (e.g. Mireot in strategies for
combining ontologies and for importing terms from
other ontologies)
46

47. Problems with the OBO Foundry
1. the results are over-complex for almost
all users
2. high quality ontology development is
slow, slow, slow
For 1., views (Brinkley, Uvic, ontodog, …)
For 2., the hub and spokes model
47

48. The Hub and Spokes Model
“Constructing a lattice of Infectious
Disease Ontologies from a
Staphylococcus aureus Isolate
Repository”
Albert Goldfain, Lindsay Cowell and Barry Smith,
Proceeedings of the Third International Conference on
Biomedical Ontology, Graz, July 22-25, 2012, forthcoming.
48

49. Infectious Disease Ontology (IDO)
– IDO Core:
• General terms in the ID domain.
• A hub for all IDO extensions.
– IDO Extensions:
• Disease specific.
• Developed by subject matter experts.
• Provides:
– Clear, precise, and consistent natural language
definitions
– Computable logical representations (OWL, OBO)

50. How IDO evolves
IDOMAL IDOCore IDOHIV
CORE and
SPOKES:
IDOFLU Domain
IDORatSa IDORatStrep ontologies
IDOSa IDOStrep
IDOMRSa IDOAntibioticResistant SEMI-LATTICE:
By subject matter
experts in different
IDOHumanSa IDOHumanStrep communities of
interest.
IDOHumanBacterial

51. IDO Core
• Contains general terms in the ID domain:
– E.g., ‘colonization’, ‘pathogen’, ‘infection’
• A contract between IDO extension ontologies
and the datasets that use them.
• Intended to represent information along
several dimensions:
– biological scale (gene, cell, organ, organism, population)
– discipline (clinical, immunological, microbiological)
– organisms involved (host, pathogen, and vector types)

52. Sample IDO Definitions
• Host of Infectious Agent (BFO Role): A role borne by
an organism in virtue of the fact that its extended
organism contains an infectious agent.
• Extended Organism (OGMS): An object aggregate
consisting of an organism and all material entities
located within the organism, overlapping the
organism, or occupying sites formed in part by the
organism.
• Infectious Agent: A pathogen whose pathogenic
disposition is an infectious disposition.

53. IDO and IDOSa
• Scale of the infection (disorder)
12/10/2010 53
from Shetty, Tang, and Andrews, 2009

54. Differentiated
Staphylococcus aureus (Sa)
by:
Antibiotic
Resistance { MSSa MRSa
Pathogenesis
Location
Type
{ HA-MRSa CA-MRSa
Geographic
Region { UK CA-MRSa
Australian
CA-MRSa
Various
Differentia { Specific Strains

55. Sample Application: A lattice of infectious disease
application ontologies from NARSA isolate data
Network on Antimicrobial Resistance
in Staphylococcus aureus
–http://www.narsa.net/content/staphLinks.jsp
True personalized medicine – YourDiseaseOntology

56. Ways of differentiating
Staphylococcus aureus infectious diseases
• Infectious Disease
– By host type
– By (sub-)species of pathogen
– By antibiotic resistance
– By anatomical site of
infection
• Bacterial Infectious Disease
– By PFGE (Strain)
– By MLST (Sequence Type)
– By BURST (Clonal Complex)
• Sa Infectious Disease
– By SCCmec type
• By ccr type
• By mec class
– spa type
http://www.sccmec.org/Pages/SCC_ClassificationEN.html

57. NRS701’s resistance to clindamycin
ido.owl
narsa.owl
ndf-rt
narsa-isolates.owl

58. R T U New York State
Center of Excellence in
Bioinformatics & Life
Sciences
Ontologies make data collections comparable
Characteristics
Cases
ch1 ch2 ch3 ch4 ch5 ch6 ...
case1
case2
case3
case4
case5
case6
...
Characteristics
Cases
ch1 ch2 ch3 ch4 ch5 ch6 ...
Characteristics case1
Cases
ch1 ch2 ch3 ch4 ch5 ch6 ... case2
case3
case1
case4
case2
case5
case3
case6
case4 ...
case5
case6 Linking the variables of distinct data collections
...
to a realism-based ontology.

59. R T U New York State
Center of Excellence in
Bioinformatics & Life
Sciences
OPMQoL: an Ontology for pain-
related disablement, mental
health and quality of life
Werner Ceusters
1R01DE021917-01A1
National Institute of Dental and
Craniofacial Research (NIDCR).

60. IASP definition for ‘pain’:
– ‘an unpleasant sensory and emotional experience
associated with actual or potential tissue damage,
or described in terms of such damage’;
which asserts:
– a common phenomenology (‘unpleasant sensory
and emotional experience’) to all instances of
pain,
– the recognition of three distinct subtypes of pain
involving, respectively:
1. actual tissue damage,
2. what is called ‘potential tissue damage’, and
3. a description involving reference to tissue damage
whether or not there is such damage.

62. A data collection consists of at least 1 data item,
each data item belonging to exactly 1 collection
1
data collection
1..*
data item

63. Data dictionaries provide information about
data items and data collections
1
explained-in data dictionary
uses 1
used-for 1..*
1
data collection
explains
1..*
1..* data item

64. Data dictionaries provide also information about
terminologies and assessment instruments used for data
generation, in addition to information about the
collection’s structure
uses 1..* used
uses for 0..*
terminology 1..* uses assessment
used
for 0..*
1..* instrument
1 used-in
explained-in data dictionary 0..*
uses 1
used-for 1..*
1
data collection
explains
1..*
1..* data item

65. Relation of Terminology component to Data
component
Terminology component uses 1..* used
uses for 0..*
terminology 1..* uses assessment
used
for 0..*
1..* instrument
1 used-in
explained-in data dictionary 0..*
uses 1 Data component
used-for 1..*
1
data collection
explains
1..*
1..* data item

66. Terminology links terms to ‘concepts’
Terminology component uses 1..* used
1 uses for 0..*
terminology 1..* uses assessment
1..* used
used in for 0..*
1..* instrument
0..*
term 1 used-in
uses 0..* expressed- explained-in data dictionary 0..*
by uses 1 Data component
1..*
used-for 1..*
1
means 1 data collection
explains
broader
concept 1..* 1..*
narrower 1..* data item
1..*

67. Not ‘concepts’ are of interest, but entities in
reality
Terminology component uses 1..* used
1 uses for 0..*
terminology 1..* uses assessment
1..* used
used in for 0..*
1..* instrument
0..*
term 1 used-in
uses 0..* expressed- explained-in data dictionary 0..*
by uses 1 Data component
1..*
used-for 1..*
1
means 1 data collection
explains
broader
concept 1..* 1..*
narrower 1..* data item
1..*
Ontology entity
component

68. It is real entities that should be denoted in
ontologies
Terminology component uses 1..* used
1 uses for 0..*
terminology 1..* uses assessment
1..* used
used in for 0..*
1..* instrument
0..*
term 1 used-in
uses 0..* expressed- explained-in data dictionary 0..*
by uses 1 Data component
1..*
used-for 1..*
1
means 1 data collection
explains
broader
concept 1..* 1..*
narrower 1..* data item
1..*
ontology
1
denotes denoted 1..*
Ontology entity 1 by 0..* denotator
component reference ontology

69. Application ontologies cover the domains of
the sources
Terminology component uses 1..* used
1 uses for 0..*
terminology 1..* uses assessment
1..* used
used in for 0..*
1..* instrument
0..*
term 1 used-in used 1
uses 0..* expressed- explained-in data dictionary 0..* for
by uses 1 Data component
1..*
used-for 1..* uses 1
1 used-for
means 1 data collection assessment
explains
1..*
broader instrument
concept 1..* 1..* uses 1
ontology
narrower 1..* data item data collection
1..*
ontology
application
ontology
ontology
1
denotes denoted 1..*
Ontology entity 1 by 0..* denotator
component reference ontology

70. Bridging axioms link data to ontologies and
terminologies
Terminology component uses 1..* used
1 uses for 0..*
terminology 1..* uses assessment
1..* used
used in for 0..*
1..* instrument
0..*
term 1 used-in used 1
uses 0..* expressed- explained-in data dictionary 0..* for
by uses 1 Data component
1..*
used-for 1..* uses 1
1 used-for
means 1 data collection assessment
explains
1..*
broader instrument
concept 1..* 1..* uses 1
ontology
expresses 0..1 narrower 1..* data item data collection
1..*
ontology
used application
uses for uses
corresponds-to representational bridging axiom
used-for ontology
1..* 0..* 1..* 1
0..* artifact
ontology
1
denotes denoted 1..*
Ontology entity 1 by 0..* denotator
component reference ontology

71. top level Basic Formal Ontology (BFO)
Ontology for
Information Artifact
Biomedical Spatial Ontology
mid-level Ontology
Investigations (BSPO)
(IAO)
(OBI)
Anatomy Ontology
(FMA*, CARO) Infectious
Disease
Environment Ontology
Cellular
Cell Ontology (IDO*)
Component
Ontology (EnvO)
Ontology
domain level (CL)
(FMA*, GO*)
Phenotypic Biological
Quality Process
Ontology Ontology (GO*)
Subcellular Anatomy Ontology (SAO) (PaTO)
Sequence Ontology
(SO*) Molecular
Function
Protein Ontology
(GO*)
(PRO*)
OBO Foundry Modular Organization 71

72. BFO: the very top
Continuant Occurrent
(Process, Event)
Independent Dependent
Continuant Continuant

73. CONTINUANT OCCURRENT
RELATION
TO TIME
INDEPENDENT DEPENDENT
GRANULARITY
Anatomical
Organism Organ
ORGAN AND Entity
(NCBI Function
ORGANISM (FMA,
Taxonomy) (FMP, CPRO) Phenotypic Biological
CARO) Quality Process
(PaTO) (GO)
CELL AND Cellular Cellular
Cell
CELLULAR Component Function
(CL)
COMPONENT (FMA, GO) (GO)
Molecule
Molecular Function Molecular Process
MOLECULE (ChEBI, SO,
(GO) (GO)
RnaO, PrO)
73

74. RELATION CONTINUANT OCCURRENT
TO TIME
GRANULARITY INDEPENDENT DEPENDENT
Anatomical
Organism Organ Organism-Level
ORGAN AND Entity
(NCBI Function Process
ORGANISM (FMA,
Taxonomy) (FMP, CPRO) Phenotypic (GO)
CARO) Quality
(PaTO)
CELL AND Cellular Cellular
Cell Cellular Process
CELLULAR Component Function
(CL) (GO)
COMPONENT (FMA, GO) (GO)
Molecule Molecular
Molecular Function
MOLECULE (ChEBI, SO, Process
(GO)
RnaO, PrO) (GO)
obofoundry.org

75. Basic Formal Ontology
continuant occurrent
independent dependent
continuant continuant
cellular molecular biological
component function processes

76. BFO: The Very Top
continuant occurrent
independent dependent
continuant continuant
quality
function
role
disposition

77. Basic Formal Ontology
types
Continuant Occurrent
process, event
Independent Dependent
Continuant Continuant
thing quality
.... ..... .......
instances

78. Basic of BFO in GO
Continuant Occurrent
biological
Independent Dependent process
Continuant Continuant
cellular molecular
..... ..... ........
component function

79. Experience with BFO in
building ontologies provides
• a community of skilled ontology developers
and users (google user group has 118
members)
• associated logical tools
• documentation for different types of users
• a methodology for building conformant
ontologies by starting with BFO and populating
downwards

80. Example: The Cell Ontology

81. Users of BFO
PharmaOntology (W3C HCLS SIG)
MediCognos / Microsoft Healthvault
Cleveland Clinic Semantic Database in Cardiothoracic
Surgery
Major Histocompatibility Complex (MHC) Ontology (NIAID)
Neuroscience Information Framework Standard (NIFSTD)
and Constituent Ontologies
Interdisciplinary Prostate Ontology (IPO)
Nanoparticle Ontology (NPO): Ontology for Cancer
Nanotechnology Research
Neural Electromagnetic Ontologies (NEMO)
ChemAxiom – Ontology for Chemistry 81
:.

82. Users of BFO
GO Gene Ontology
CL Cell Ontology
SO Sequence Ontology
ChEBI Chemical Ontology
PATO Phenotype (Quality) Ontology
FMA Foundational Model of Anatomy Ontology
ChEBI Chemical Entities of Biological Interest
PRO Protein Ontology
Plant Ontology
Environment Ontology
Ontology for Biomedical Investigations
RNA Ontology 82
:.

83. Users of BFO
Ontology for Risks Against Patient Safety (RAPS/REMINE)
eagle-i an VIVO (NCRR)
IDO Infectious Disease Ontology (NIAID)
National Cancer Institute Biomedical Grid Terminology
(BiomedGT)
US Army Biometrics Ontology
US Army Command and Control Ontology
Sleep Domain Ontology
Subcellular Anatomy Ontology (SAO)
Translaftional Medicine On (VO)
Yeast Ontology (yOWL)
Zebrafish Anatomical Ontology (ZAO) 83
:.

84. Basic Formal Ontology
continuant occurrent
independent dependent
continuant continuant
organism
84

85. Continuants
• continue to exist through time,
preserving their identity while
undergoing different sorts of changes
• independent continuants – objects,
things, ...
• dependent continuants – qualities,
attributes, shapes, potentialities ...
85

86. Occurrents
• processes, events, happenings
– your life
– this process of accelerated cell division
86

87. Qualities
temperature
blood pressure
mass
...
are continuants
they exist through time while
undergoing changes
87

88. Qualities
temperature / blood pressure / mass ...
are dimensions of variation within the
structure of the entity
a quality is something which can
change while its bearer remains one
and the same
88

89. A Chart representing how
John’s temperature changes
89

90. A Chart representing how
John’s temperature changes
90

91. John’s temperature,
the temperature he has throughout his
entire life, cycles through different
determinate temperatures from one
time to the next
John’s temperature is a physiology
variable which, in thus changing,
exerts an influence on other physiology
variables through time
91

92. BFO: The Very Top
continuant occurrent
independent dependent
continuant continuant
quality
temperature 92

93. Blinding Flash of the Obvious
independent dependent
continuant continuant
quality
organism
temperature types
John’s
John
temperature
instances
93

94. Blinding Flash of the Obvious
independent dependent
continuant continuant
quality
organism
temperature types
John’s
John
temperature
instances
94

95. Blinding Flash of the Obvious
inheres_in
.
organism
temperature types
John’s
John
temperature
instances
95

96. temperature types
37ºC 37.1ºC 37.2ºC 37.3ºC 37.4ºC 37.5ºC
instantiates instantiates instantiates instantiates instantiates instantiates
at t1 at t2 at t3 at t4 at t5 at t6
John’s temperature
instances 96

97. human types
embryo fetus neonate infant child adult
instantiates instantiates instantiates instantiates instantiates instantiates
at t1 at t2 at t3 at t4 at t5 at t6
John
instances 97

98. Temperature subtypes
Development-stage subtypes
are threshold divisions (hence we do
not have sharp boundaries, and we
have a certain degree of choice, e.g. in
how many subtypes to distinguish,
though not in their ordering)
98

99. independent dependent
continuant continuant
quality
organism
temperature types
John’s
John
temperature
instances
99

100. independent dependent
occurrent
continuant continuant
quality process
organism course of
temperature temperature
changes
John’s John’s
John
temperature temperature history
100

101. independent dependent
occurrent
continuant continuant
quality process
organism
temperature temperature
process profile
John’s John’s
John
temperature temperature history

102. independent dependent
occurrent
continuant continuant
quality process
organism
temperature life of an
organism
John’s John’s
John
temperature life
102

103. BFO: The Very Top
continuant occurrent
independent dependent
continuant continuant
quality disposition
103

104. Disposition
- of a glass vase, to shatter if dropped
- of a human, to eat
- of a banana, to ripen
- of John, to lose hair
104

105. Disposition
if it ceases to exist, then its bearer
and/or its immediate surrounding
environment is physically changed
its realization occurs when its bearer is in
some special physical circumstances
its realization is what it is in virtue of the
bearer’s physical make-up
105

106. Function
- of liver: to store glycogen
- of birth canal: to enable transport
- of eye: to see
- of mitochondrion: to produce ATP
functions are dispositions which are
designed or selected for
106
:.

107. independent dependent
occurrent
continuant continuant
function process
eye
to see process of
seeing
John’s eye function of John’s John seeing
eye: to see
107

108. OGMS
Ontology for General Medical
Science
http://code.google.com/p/ogms
108

109. Physical Disorder
109

110. Physical Disorder
– independent
continuant
fiat object part
A causally linked
combination of physical
components of the
extended organism that
is clinically abnormal. 110
:.

111. Clinically abnormal
– (1) not part of the life plan for an organism
of the relevant type (unlike aging or
pregnancy),
– (2) causally linked to an elevated risk
either of pain or other feelings of illness,
or of death or dysfunction, and
– (3) such that the elevated risk exceeds a
certain threshold level.*
*Compare: baldness
111

112. Big Picture
112

113. http://code.google.com/p/ogms
Disease =def. – A disposition to undergo
pathological processes that exists in an
organism because of one or more
disorders in that organism.
Disease course =def. – The aggregate of
processes in which a disease disposition
is realized.
113

114. Pathological Process
=def. A bodily process that is a
manifestation of a disorder and is clinically
abnormal.
Disease =def. – A disposition to undergo
pathological processes that exists in an
organism because of one or more
disorders in that organism.
114

115. Cirrhosis - environmental exposure
• Etiological process - phenobarbitol-induced hepatic cell death
– produces
• Disorder - necrotic liver
– bears
• Disposition (disease) - cirrhosis
– realized_in
• Pathological process - abnormal tissue repair with cell proliferation
and fibrosis that exceed a certain threshold; hypoxia-induced cell
death
– produces
• Abnormal bodily features
– recognized_as
• Symptoms - fatigue, anorexia
• Signs - jaundice, enlarged spleen
115

116. Influenza - infectious
• Etiological process - infection of airway epithelial cells with
influenza virus
– produces
• Disorder - viable cells with influenza virus
– bears
• Disposition (disease) - flu
– realized_in
• Pathological process - acute inflammation
– produces
• Abnormal bodily features
– recognized_as
• Symptoms - weakness, dizziness
• Signs - fever 116

117. Dispositions and Predispositions
All diseases are dispositions; not all
dispositions are diseases.
Predisposition to Disease
=def. – A disposition in an organism that
constitutes an increased risk of the
organism’s subsequently developing some
disease.
117

118. Huntington’s Disease - genetic
• Etiological process - inheritance of  Symptoms & Signs
>39 CAG repeats in the HTT gene  used_in
– produces  Interpretive process
• Disorder - chromosome 4 with
 produces
abnormal mHTT
– bears  Hypothesis - rule out Huntington’s
• Disposition (disease) - Huntington’s  suggests
disease  Laboratory tests
– realized_in  produces
• Pathological process - accumulation of
mHTT protein fragments, abnormal
 Test results - molecular detection of
transcription regulation, neuronal cell the HTT gene with >39CAG repeats
death in striatum  used_in
– produces  Interpretive process
• Abnormal bodily features  produces
– recognized_as  Result - diagnosis that patient X has a
• Symptoms - anxiety, depression disorder that bears the disease
• Signs - difficulties in speaking and Huntington’s disease
swallowing

119. HNPCC - genetic pre-disposition
• Etiological process - inheritance of a mutant mismatch repair gene
– produces
• Disorder - chromosome 3 with abnormal hMLH1
– bears
• Disposition (disease) - Lynch syndrome
– realized_in
• Pathological process - abnormal repair of DNA mismatches
– produces
• Disorder - mutations in proto-oncogenes and tumor suppressor genes
with microsatellite repeats (e.g. TGF-beta R2)
– bears
• Disposition (disease) - non-polyposis colon cancer

120. Systemic arterial hypertension
• Etiological process – abnormal  Symptoms & Signs
reabsorption of NaCl by the kidney  used_in
– produces  Interpretive process
 produces
• Disorder – abnormally large scattered
molecular aggregate of salt in the  Hypothesis - rule out hypertension
blood  suggests
– bears  Laboratory tests
 produces
• Disposition (disease) - hypertension
– realized_in  Test results -
 used_in
• Pathological process – exertion of
abnormal pressure against arterial wall
 Interpretive process
 produces
– produces
 Result - diagnosis that patient X has a
• Abnormal bodily features
disorder that bears the disease hypertension
– recognized_as
• Symptoms -
• Signs – elevated blood pressure

121. Type 2 Diabetes Mellitus
• Etiological process –  Symptoms & Signs
 used_in
– produces
• Disorder – abnormal pancreatic beta
 Interpretive process
 produces
cells and abnormal muscle/fat cells
– bears  Hypothesis - rule out diabetes mellitus
 suggests
• Disposition (disease) – diabetes
mellitus
 Laboratory tests – fasting serum blood
glucose, oral glucose challenge test, and/or
– realized_in blood hemoglobin A1c
• Pathological processes – diminished  produces
insulin production , diminished  Test results -
muscle/fat uptake of glucose  used_in
– produces  Interpretive process
• Abnormal bodily features  produces
– recognized_as  Result - diagnosis that patient X has a
• Symptoms – polydipsia, polyuria, disorder that bears the disease type 2
polyphagia, blurred vision diabetes mellitus
• Signs – elevated blood glucose and
hemoglobin A1c

122. Type 1 hypersensitivity to penicillin
• Etiological process – sensitizing of mast  Symptoms & Signs
cells and basophils during exposure to  used_in
penicillin-class substance  Interpretive process
– produces  produces
• Disorder – mast cells and basophils with  Hypothesis -
epitope-specific IgE bound to Fc epsilon  suggests
receptor I  Laboratory tests –
– bears  produces
• Disposition (disease) – type I  Test results – occasionally, skin testing
hypersensitivity  used_in
– realized_in  Interpretive process
•  produces
Pathological process – type I
hypersensitivity reaction  Result - diagnosis that patient X has a
– produces disorder that bears the disease type 1
hypersensitivity to penicillin
• Abnormal bodily features
– recognized_as
• Symptoms – pruritis, shortness of breath
• Signs – rash, urticaria, anaphylaxis

123. Early Onset Alzheimer’s Disease
Disorder – mutations in APP, PSEN1 and PSEN2
bears
Disposition – impaired APP processing
realized in
Pathological process – accumulation of intra- and extracellular protein in the
brain
produces
Disorder – amyloid plaque and neurofibrillary tangles
bears
Disposition – of neurons to die
realized in
Pathological process – neuronal loss
produces
Disorder – cognitive brain regions damaged and reduced in size
bears
Disposition (disease) – Alzheimer’s dementia
realized in
Symptoms – episodic memory loss and other cognitive domain impairment
123

124. Arterial Aneurysm
• Disposition – atherosclerosis
– realized in
• Pathological process – fatty material collects within the walls of arteries
– produces
• Disorder – artery with weakened wall
– bears
• Disposition – of artery to become distended
– realized_in
• Pathological process – process of distending
– produces
• Disorder – arterial aneurysm
– bears
• Disposition – of artery to rupture
– realized in
• Pathological process – (catastrophic event) of rupturing
– produces
• Disorder – ruptured artery, arterial system with dangerously low blood pressure
– bears
• Disposition – circulatory failure
– realized in
• Pathological process – exsanguination, failure of homeostasis
– produces
• Death
124

125. Hemorrhagic stroke
• Disorder – cerebral arterial aneurysm
– bears
• Disposition – of weakened artery to rupture
– realized in
• Pathological process – rupturing of weakened blood vessel
– produces
• Disorder – Intraparenchymal cerebral hemorrhage
– bears
• Disposition (disease) – to increased intra-cranial pressure
– realized in
• Pathological process – increasing intra-cranial pressure, compression of brain
structures
– produces
• Disorder – Cerebral ischemia, Cerebral neuronal death
– bears
• Disposition (disease) – stroke
– realized in
• Symptoms – weakness/paralysis, loss of sensation, etc
125

126. 126

127. coronary heart
disease
early lesions asymptomatic surface
unstable stable
and small (‘silent’) disruption of
angina angina
fibrous plaques infarction plaque
instantiates instantiates instantiates instantiates instantiates
at t1 at t2 at t3 at t4 at t5
John’s coronary heart disease
127
time

128. independent dependent
occurrent
continuant continuant
disposition process
disorder course of
disease
disease
John’s John’s
disordered coronary heart course of John’s
heart disease disease
128