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Title: Ontology Tutorial Part 1 What is Ontology and What Can It Do


1
Ontology Tutorial Part 1What is Ontology and
What Can It Do?
Barry Smith http//ontology.buffalo.edu/smith
2
The problem of data integration / information
fusion
  • About 30,000 genes in a human
  • Probably 100-200,000 proteins
  • Individual variation in most genes
  • 100s of cell types
  • 100,000s of disease types

3
Organism
Organ
Tissue
Cell
Organelle
Protein
DNA
4
The Challenge
  • Each (clinical, pathological, genetic,
    proteomic, pharmacological ) information system
    uses its own terminology and category system
  • biomedical research demands the ability to
    navigate through all such information systems
  • How can we overcome the incompatibilities which
    become apparent when data from distinct sources
    is combined?

5
Answer
  • Ontology

6
Three senses of ontology
  • Philosophical sense an inventory of the types of
    entities and relations in reality
  • Knowledge engineering sense an ontology as a
    consensus representation of the concepts used in
    a given domain
  • (Semantic Web)
  • 3. Ontology as controlled vocabulary
  • (Gene Ontology, Open Biological Ontologies
    Consortium)

7
Three senses of ontology
  • Philosophical sense an inventory of the types of
    entities and relations in reality
  • Knowledge engineering sense an ontology as a
    consensus representation of the concepts used in
    a given domain
  • (Semantic Web)
  • 3. Ontology as controlled vocabulary
  • (Gene Ontology, Open Biological Ontologies
    Consortium)

8
Ontology as a branch of philosophy
  • seeks to establish
  • the basic formal-ontological structures
  • the kinds and structures of objects, properties,
    events, processes and relations in each material
    domain of reality

9
Formal ontology an analogue of pure mathematics
  • Can be applied to different domains

10
Material ontology a kind of generalized chemistry
or zoology
  • (Aristotles ontology grew out of biological
    classification)

11
Aristotle
worlds first ontologist

12
Worlds first ontology (from Porphyrys
Commentary on Aristotles Categories)
13
Linnaean Ontology
14
Formal Ontology
  • theory of part and whole
  • theory of dependence / unity
  • theory of boundary, continuity and contact
  • theory of universals and instances
  • theory of continuants and occurrents (objects and
    processes)
  • theory of functions and functioning
  • theory of granularity

15
Formal Ontology
  • the theory of those ontological structures
  • (such as part-whole, universal-particular)
  • which apply to all domains whatsoever

16
Formal-Ontological Categories
  • substance
  • process
  • function
  • unity
  • plurality
  • site
  • dependent part
  • independent part
  • are able to form complex structures in
    non-arbitrary ways joined by relations such as
    part, dependence, location.

17
A Network of Domain Ontologies
Basic Formal Ontology
  • Material (Regional) Ontologies

18
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19
Three senses of ontology
  • Philosophical sense an inventory of the types of
    entities and relations in reality
  • Knowledge engineering sense an ontology as a
    consensus representation of the concepts used in
    a given domain
  • (Semantic Web)
  • 3. Ontology as controlled vocabulary
  • (Gene Ontology, Open Biological Ontologies
    Consortium)

20
Assumptions
  • Communication / compatibility problems should be
    solved automatically
  • (by machine)
  • Hence ontologies must be applications running in
    real time

21
Application ontology
  • Ontologies are inside the computer
  • thus subject to severe constraints on expressive
    power
  • (effectively the expressive power of Description
    Logic)

22
Problem Confusion of concepts and entities in
reality
  • Dont construct theories of reality construct
    models of concepts

23
The Semantic Web
Ontology in the Knowledge Engineering Sense
24
A new silver bullet

25
The Semantic Web
  • designed to integrate the vast amounts of
    heterogeneous online data and services
  • via dramatically better support at the level of
    metadata designed to yield the ability to query
    and integrate across different conceptual systems

26
Tim Berners-Lee, inventor of the internet
  • sees a more powerful Web emerging, one where
    documents and data will be annotated with special
    codes allowing computers to search and analyze
    the Web automatically. The codes are designed
    to add meaning to the global network in ways that
    make sense to computers

27
hyperlinked vocabularies, called ontologies
will be used by Web authors
  • to explicitly define their words and concepts
    as they post their stuff online.
  • The idea is the codes would let software
    "agents" analyze the Web on our behalf, making
    smart inferences that go far beyond the simple
    linguistic analyses performed by today's search
    engines.

28
Exploiting tools such as
  • XML
  • OWL (Ontology Web Language)
  • RDF (Resource Descriptor Framework)
  • DAML-OIL (Darpa Agent Mark-Up Language
    Ontology Inference Layer)
  • (confusing syntactic integration with semantic
    integration)

29
  • Ontology confused with the language of ontology
  • Ontology for semantic webbers is without
    content
  • Philosophical ontology build a theory of
    reality
  • Semantic-web-style ontology build a model of
    the data in our computers

30
Defining gene
  • GDB a gene is a DNA fragment that can be
    transcribed and translated into a protein
  • Genbank a gene is a DNA region of biological
    interest with a name and that carries a genetic
    trait or phenotype

31
Example The Enterprise Ontology
  • A Sale is an agreement between two Legal-Entities
    for the exchange of a Product for a Sale-Price.
  • A Strategy is a Plan to Achieve a high-level
    Purpose.
  • A Market is all Sales and Potential Sales within
    a scope of interest.

32
Example Statements of Accounts
  • Company Financial statements may be prepared
    under either the (US) GAAP or the (European) IASC
    standards
  • These allocate cost items to different
    categories depending on the laws of the countries
    involved.

33
Job
  • to develop an algorithm for the automatic
    conversion of income statements and balance
    sheets between the two systems.
  • Not even this relatively simple problem has been
    satisfactorily resolved
  • why not?
  • Because the very same terms mean different
    things
  • and are applied in different ways
  • in different cultures

34
The Semantic Web Initiative
  • The Web is a vast edifice of heterogeneous data
    sources
  • Needs the ability to query and integrate across
    different conceptual systems

35
How resolve incompatibilities?
  • enforce terminological compatibility via
    standardized term hierarchies, with standardized
    definitions of terms, which
  • 1. satisfy the constraints of a description
    logic (DL)
  • 2. are applied as meta-tags to the content of
    websites

36
Clay Shirky
  • The Semantic Web is a machine for creating
    syllogisms.
  • Humans are mortalGreeks are humanTherefore,
    Greeks are mortal

37
Lewis Carroll
  • - No interesting poems are unpopular among
    people of real taste - No modern poetry is free
    from affectation - All your poems are on the
    subject of soap-bubbles - No affected poetry is
    popular among people of real taste - No ancient
    poetry is on the subject of soap-bubbles
  • Therefore All your poems are bad.

38
the promise of the Semantic Web
  • it will improve all the areas of your life where
    you currently use syllogisms

39
We can use the Semantic Webto prove that Joe
loves Mary
  • we found two documents on a trusted site, one of
    which said that "Joe loves MJS", and another
    of which said that "MJS damlequivalentTo
    Mary". We also got the checksums of the files in
    person from the maintainer of the site. To
    check this information, we can list the checksums
    in a local file, and then set up some FOPL rules
    that say "if file 'a' contains the information
    Joe loves mary and has the checksum
    md50qrhf8q3hfh, then record SuccessA", "if file
    'b' contains the information MJS is equivalent to
    Mary, and has the checksum md50892t925h, then
    record SuccessB", and "if SuccessA and SuccessB,
    then Joe loves Mary". http//infomesh.net/2001/sw
    intro/

40
Merging Databases
  • Merging databases simply becomes a matter of
    recording in RDF somewhere that "Person Name" in
    your database is equivalent to "Name" in my
    database, and then throwing all of the
    information together and getting a processor to
    think about it. http//infomesh.net/2001/swintro/
  • Is your "Person Name John Smith" the same
    person as my "Name John Q. Smith"? Who knows?
    Not the Semantic Web

41
XML-syntax does not help
  • ltBUSINESS-CARDgt  ltFIRSTNAMEgtJuleslt/FIRSTNAMEgt
     ltLASTNAMEgtDerycklt/LASTNAMEgt  ltCOMPANYgtNewcolt/CO
    MPANYgt  ltMEMBEROFgtXTC Grouplt/MEMBEROFgt
     ltJOBTITLEgtBusiness Managerlt/JOBTITLEgt
     ltTELgt32(0)3.471.99.60lt/TELgt  ltFAXgt32(0)3.891.
    99.65lt/FAXgt  ltGSMgt32(0)465.23.04.34lt/GSMgt
     ltWEBSITEgtwww.newco.comlt/WEBSITEgt  ltADDRESSgt  
    ltSTREETgtDendersesteenweg 17lt/STREETgt  
    ltZIPgt2630lt/ZIPgt   ltCITYgtAartselaarlt/CITYgt  
    ltCOUNTRYgtBelgiumlt/COUNTRYgt  lt/ADDRESSgt
    lt/BUSINESS-CARDgt

42
and with correct XML-syntax
  • ltBUSINESS-CARDgt  ltFIRSTNAMEgtJuleslt/FIRSTNAMEgt
     ltLASTNAMEgtDerycklt/LASTNAMEgt  ltCOMPANYgtNewcolt/CO
    MPANYgt  ltMEMBEROFgtXTC Grouplt/MEMBEROFgt
     ltJOBTITLEgtBusiness Managerlt/JOBTITLEgt
     ltTELgt32(0)3.471.99.60lt/TELgt  ltFAXgt32(0)3.891.
    99.65lt/FAXgt  ltGSMgt32(0)465.23.04.34lt/GSMgt
     ltWEBSITEgtwww.newco.comlt/WEBSITEgt  ltADDRESSgt  
    ltSTREETgtDendersesteenweg 17 lt/STREETgt  

43
and with correct XML-syntax
  • ltBUSINESS-CARDgt  ltFIRSTNAMEgtJuleslt/FIRSTNAMEgt
     ltLASTNAMEgtDerycklt/LASTNAMEgt  ltCOMPANYgtNewcolt/CO
    MPANYgt  ltMEMBEROFgtXTC Grouplt/MEMBEROFgt
     ltJOBTITLEgtBusiness Managerlt/JOBTITLEgt
     ltTELgt32(0)3.471.99.60lt/TELgt  ltFAXgt32(0)3.891.
    99.65lt/FAXgt  ltGSMgt32(0)465.23.04.34lt/GSMgt
     ltWEBSITEgtwww.newco.comlt/WEBSITEgt  ltADDRESSgt  
    ltSTREETgtDendersesteenweg 17lt/STREETgt  
    ltZIPgt2630lt/ZIPgt   ltCITYgtAartselaarlt/CITYgt  
    ltCOUNTRYgtBelgiumlt/COUNTRYgt  lt/ADDRESSgt
    lt/BUSINESS-CARDgt

44
and with correct XML-syntax
Is Jules or Newco the member of XTC Group?
  • ltBUSINESS-CARDgt  ltFIRSTNAMEgtJuleslt/FIRSTNAMEgt
     ltLASTNAMEgtDerycklt/LASTNAMEgt  ltCOMPANYgtNewcolt/CO
    MPANYgt  ltMEMBEROFgtXTC Grouplt/MEMBEROFgt
     ltJOBTITLEgtBusiness Managerlt/JOBTITLEgt
     ltTELgt32(0)3.471.99.60lt/TELgt  ltFAXgt32(0)3.891.
    99.65lt/FAXgt  ltGSMgt32(0)465.23.04.34lt/GSMgt
     ltWEBSITEgtwww.newco.comlt/WEBSITEgt  ltADDRESSgt  
    ltSTREETgtDendersesteenweg 17lt/STREETgt  
    ltZIPgt2630lt/ZIPgt   ltCITYgtAartselaarlt/CITYgt  
    ltCOUNTRYgtBelgiumlt/COUNTRYgt  lt/ADDRESSgt
    lt/BUSINESS-CARDgt

45
and with correct XML-syntax
  • ltBUSINESS-CARDgt  ltFIRSTNAMEgtJuleslt/FIRSTNAMEgt
     ltLASTNAMEgtDerycklt/LASTNAMEgt  ltCOMPANYgtNewcolt/CO
    MPANYgt  ltMEMBEROFgtXTC Grouplt/MEMBEROFgt
     ltJOBTITLEgtBusiness Managerlt/JOBTITLEgt
     ltTELgt32(0)3.471.99.60lt/TELgt  ltFAXgt32(0)3.891.
    99.65lt/FAXgt  ltGSMgt32(0)465.23.04.34lt/GSMgt
     ltWEBSITEgtwww.newco.comlt/WEBSITEgt  ltADDRESSgt  
    ltSTREETgtDendersesteenweg 17lt/STREETgt  
    ltZIPgt2630lt/ZIPgt   ltCITYgtAartselaarlt/CITYgt  
    ltCOUNTRYgtBelgiumlt/COUNTRYgt  lt/ADDRESSgt
    lt/BUSINESS-CARDgt

Do the phone numbers and address belong to Jules
or to the business?
46
Shirkey
  • The Semantic Web's philosophical argument -- the
    world should make more sense than it does -- is
    hard to argue with. The Semantic Web, with its
    neat ontologies and its syllogistic logic, is a
    nice vision. However, like many visions that
    project future benefits but ignore present costs,
    it requires too much coordination and too much
    energy to be effective in the real world

47
Semantic Web effort
  • thus far devoted primarily to developing systems
    for standardized representation of web pages and
    web processes
  • ( ontology of web typography)
  • not to the harder task of developing of
    ontologies (term hierarchies) for the content of
    such web pages

48
Cory Doctorow
  • A world of exhaustive, reliable metadata would
    be a utopia.

49
Problem 1 People lie
  • Meta-utopia is a world of reliable metadata.
  • But poisoning the well can confer benefits to
    the poisoners
  • Metadata exists in a competitive world.
  • Some people are crooks.
  • Some people are cranks.
  • Some people are French philosophers.

50
Problem 2 People are lazy
  • Half the pages on Geocities are called Please
    title this page

51
Problem 3 People are stupid
  • The vast majority of the Internet's users
  • (even those who are native speakers of English)
  • cannot spell or punctuate
  • Will internet users learn to accurately tag
    their information with whatever DL-hierarchy
    they're supposed to be using?

52
Problem 4 Ontology Impedance
  • semantic mismatch between ontologies being
    merged
  • This problem recognized in Semantic Web
    literature
  • http//ontoweb.aifb.uni-karlsruhe.de
  • /About/Deliverables/ontoweb-del-7.6-swws1.pdf

53
Solution 1treat it as (inevitable) impedance
  • and learn to find ways to cope with the
    disturbance which it brings
  • Suggested here
  • http//ontoweb.aifb.uni-karls-ruhe.de/Ab-out/Deliv
    erables/ontoweb-del-7.6-swws1.pdf

54
Solution 2 resolve the impedance problem on a
case-by-case basis
  • Suppose two databases are put on the web.
  • Someone notices that "where" in the friends
    table and "zip" in the places table mean the same
    thing.
  • http//www.w3.org/DesignIssues/Semantic.html

55
Both solutions fail
  • 1. treating mismatches as impedance ignores
    the problem of error propagation
  • (and is inappropriate in an area like medicine)
  • 2. resolving impedance on a case-by-case basis
    defeats the very purpose of the Semantic Web

56
Ontology Impedance
  • gene used in websites issued by
  • biotech companies involved in gene patenting
  • medical researchers interested in role of genes
    in predisposition to smoking
  • insurance companies

57
The idea
  • distinguish two separate tasks
  • developing an expressively rich correct
    ontologies of given domains
  • - developing on this basis computer applications
    capable of running in real time

58
Basic Formal Ontology
  • BFO
  • The Vampire Slayer

59
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60
BFO
  • ontology not the standardization or
    specification of concepts
  • (not a branch of knowledge or concept
    engineering)
  • but an inventory of the types of entities
    existing in reality

61
BFO not a computer application
  • but a reference ontology
  • in the sense of Aristotelian philosophy
  • - it sacrifices tractability for the sake of
    expressive power

62
Defining gene
  • GDB a gene is a DNA fragment that can be
    transcribed and translated into a protein
  • Genbank a gene is a DNA region of biological
    interest with a name and that carries a genetic
    trait or phenotype

63
Ontology
  • fragment, region, name, carry, trait,
    type
  • ... part, whole, function, inhere,
    substance
  • are ontological terms in the sense of traditional
    (philosophical) ontology

64
BFO
  • not just a system of categories
  • but a formal theory
  • with definitions, axioms, theorems
  • designed to provide formal resources for the
    building of reference ontologies for specific
    domains
  • the latter should be of sufficient richness that
    terminological incompatibilities can be resolved
    intelligently rather than by brute force

65
The Reference Ontology Community
  • IFOMIS (Saarbrücken)
  • Laboratories for Applied Ontology (Trento/Rome,
    Turin)
  • Foundational Ontology Project (Leeds)
  • Ontology Works (Baltimore
  • Department of Structural Biology (Seattle)
  • Virtual Soldier Project (DARPA)
  • Open Biological Ontologies Consortium (Cambridge,
    Berkeley, Bar Harbor)

66
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67
Ontology Tutorial Part 2The Future of Ontology
in Biomedicine
68
Ontology Tutorial Part 2The Future of Ontology
in Buffalo
69
Ontology Tutorial Part 2The Future of Ontology
in Biomedicine
70
Three senses of ontology
  • Philosophical sense an inventory of the types of
    entities and relations in reality
  • Knowledge engineering sense an ontology as a
    consensus representation of the concepts used in
    a given domain
  • (Semantic Web)
  • 3. Ontology as controlled vocabulary
  • (Gene Ontology, Open Biological Ontologies
    Consortium)

71
Philosophical Ontology
  • Ontologies are WINDOWS ON REALITY
  • Ontologies deal with classes/universals/invariants
    in reality
  • which exist independently of our theorizing
  • and independently of our language

72
What are universals?
  • invariants in reality
  • satisfying biological laws
  • (there are truths about universals in biological
    textbooks)

73
  • A universal is not determined by its instances as
    a state is not determined by its citizens
  • A universal may vary with time as an organism may
    vary with time (by gaining and losing molecules)

74
Universals are Not Sets
  • A set is an abstract structure, existing outside
    time and space. The set of Romans timelessly has
    Julius Caesar as a member.
  • Universals exist in time.

75
A Window on Reality
76
Medical Diagnostic Hierarchy
a hierarchy in the realm of diseases
77
Dependence Relations
Organisms
Diseases
78
A Window on Reality
Organisms
Diseases
79
A Window on Reality
80
universals
mammal
frog
instances
81
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82
Many current standard ontologies ramshackle
because they have no counterpart of formal
ontology
  • The Universal Medical Language System (UMLS)
  • a compendium of source vocabularies including
  • HL7 RIM
  • SNOMED
  • International Classification of Diseases
  • MeSH Medical Subject Headings
  • Gene Ontology

83
Three senses of ontology
  • Philosophical sense an inventory of the types of
    entities and relations in reality
  • Knowledge engineering sense an ontology as a
    consensus representation of the concepts used in
    a given domain
  • (Semantic Web)
  • 3. Ontology as controlled vocabulary
  • (Gene Ontology, Open Biological Ontologies
    Consortium)

84
Problem The different source vocabularies are
incompatible with each other
85
Problem They contain bad coding
  • which often derives from failure to pay
    attention to simple logical or ontological
    principles or from principles of good definitions

86
Bad Coding
  • Plant roots is-a Plant
  • Plant leaves is-a Plant
  • Pollen is-a Plant
  • Both testes is a testis
  • Both uterii is a uterus

87
Bad definitions
  • Heptolysis def the cause of heptolysis
  • Biological process def a biological goal that
    requires more than one function

88
The Concept Orientation
  • Work on biomedical ontologies grew out of work on
    medical dictionaries and nomenclatures
  • Has focused almost exclusively on concepts
    conceived (sometimes confused with
    terms/descriptions).

89
The Curse of Linguistics
  • Work on biomedical ontologies grew out of work on
    medical dictionaries and nomenclatures
  • This led to the assumption that all that need be
    said about classes can be said without appeal to
    time or to instances in reality.
  • Ontology is about meanings/terms/strings

90
An alternative research programme for
ontologybased on philosophical principles
  • Terms in bio-ontologies refer not to concepts
  • but to universals in reality

91
already reformed
  • Foundational Model of Anatomy Anatomy Reference
    Ontology

92
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93
A window on reality
94
Anatomical Space
Anatomical Structure
Organ Cavity Subdivision
Organ Cavity
Organ
Serous Sac
Organ Component
Serous Sac Cavity
Tissue
Serous Sac Cavity Subdivision
Pleural Sac
Pleura(Wall of Sac)
Pleural Cavity
Parietal Pleura
Visceral Pleura
Interlobar recess
Mediastinal Pleura
Mesothelium of Pleura
95
To represent ontological relations we need to
take instances into account
  • To say A part_of B is not to say anything about
    Bs need for As as parts

96
part_of as a relation between universals
  • A part_of B def
  • given any x, if inst(x, A) then there is some y
    such that inst(y, B) and part(x, y)
  • human testis part_of human being,
  • But not
  • heart part_of human being.

97
already reformed
  • Foundational Model of Anatomy Anatomy Reference
    Ontology

98
  • under construction / overhaul
  • Physiology Reference Ontology
  • Gene Ontology
  • OBOL

99
The Gene Ontology
  • a controlled vocabulary for annotations of genes
    and gene products

100
When a gene is identified
  • three important 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?

101
GO has three ontologies
102
GO astonishingly influential
  • used by all major species genome projects
  • used by all major pharmacological research groups
  • used by all major bioinformatics research groups

103
GO part of the Open Biological Ontologies
consortium
104
Each of GOs ontologies
  • is organized in a graph-theoretical structure
    involving two sorts of links or edges
  • is-a ( is a subtype of )
  • (copulation is-a biological process)
  • part-of
  • (cell wall part-of cell)

105
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106
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107
  • cellular components
  • molecular functions
  • biological processes
  • 1372 component terms
  • 7271 function terms
  • 8069 process terms

108
The Cellular Component Ontology (counterpart of
anatomy)
  • flagellum
  • chromosome
  • membrane
  • cell wall
  • nucleus

109
The Molecular Function Ontology
  • ice nucleation
  • protein stabilization
  • kinase activity
  • binding
  • The Molecular Function ontology is (roughly) an
    ontology of actions on the molecular level of
    granularity

110
Biological Process Ontology
  • glycolysis
  • copulation
  • death
  • An ontology of occurrents on the level of
    granularity of cells, organs and whole organisms

111
GO built by biologists
  • free of the Curse of Linguistics
  • free of the Curse of Computer Science

112
but problems still remain
  • menopause part_of aging
  • aging part_of death
  • menopause part_of death

113
heptolysis
  • Definition
  • The causes of heptolysis

114
  • regulation of sleep part_of sleep
  • extrinsic to membrane part_of membrane

115
GO uses only two relations
  • is_a and part_of

116
hence GO has only sentences of the forms A is_a B
and A part_of B
  • no way to express not and no way to express
    is localized at and no way to express I dont
    know

117
  • Holliday junction helicase complex
  • is-a
  • unlocalized
  • cellular component unknown is-a cellular
    component

118
Old GO definition of part_of
  • A part_of B def A can be part of B

119
New GO definition of part_of as part of current
OBOL reform effort
  • A part_of B def
  • given any x, if inst(x, A) then there is some y
    such that inst(y, B) and part(x, y)

120
Analogous problems for nearly all foundational
relations of ontologies and semantic networks
  • A causes B
  • A is associated with B
  • A is located in B
  • etc.
  • Reference to instances is necessary to clear up
    these problems

121
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122
The Future of Ontology in Buffalo
  • http//ontology.buffalo.edu/bcor/
  • to provide a forum within which philosophical
    ontologists and those involved in ontology
    applications can work together in high-level
    interdisciplinary research
  • to assist in coordination and integration of
    projects in ontological research being pursued in
    Buffalo

123
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124
  • College of Arts and Sciences
  • Computer Science and Engineering
  • School of Management
  • Center of Excellence in Bioinformatics
  • School of Informatics
  • School of Dental Medicine
  • Center for Multisource Information Fusion
  • National Center for Geographic Information and
    Analysis
  • School of Medicine and Biomedical Sciences

125
  • Computer Science and Engineering
  • School of Management
  • Charles Dement
  • Pharma of the Future

126
  • Computer Science and Engineering
  • Daniel Fischer
  • Bill Rapaport
  • Stuart Shapiro
  • Rohini Srihari

127
School of Management
  • Ram Ramesh
  • Rajiv Kishore

128
Center of Excellence in Bioinformatics
  • Daniel Fischer

129
School of Informatics / School of Medicine
  • Gary Byrd
  • Medical Informatics Certificate Program

130
School of Dental Medicine
  • John Eisner
  • Louis Goldberg
  • SNODENT

131
Center for Multisource Information Fusion
  • Eric Little
  • James Llinas
  • Galina Rogova
  • Moises Sudit

132
National Center for Geographic Information and
Analysis
  • David Mark
  • Barry Smith

133
Department of Philosophy
  • Barry Smith (Director?)
  • Randall Dipert
  • Jorge Gracia 
  • David Hershenov
  • Ingvar Johansson
  • Jiyuan Yu

134
Goal
  • To show how philosophical ontology can
    contribute to the successful application of
    ontologies in information systems
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