We can distinguish two families of approaches to the building of ontologies -- corresponding roughly to the contrast between 'neats' and 'scruffies' in artificial intelligence research. We describe the implications of each approach for the building of an ontology of philosophy, focusing especially on the Indiana Philosophy Ontology (InPhO) project led by Colin Allen. A video presentation based on these slides is available here: https://www.youtube.com/watch?v=5HV3M0NvyPM

1. Towards an Ontology of
Philosophy
Barry Smith
http://ontology.buffalo.edu/smith
APA, Vancouver, April 2, 2015

2. World’s most successful ontology

3. “Siri: An Ontology-driven Application for the
Masses”, A. Cheyer and T. Gruber (2010)
3

4. 4
Aristotle's Ontology of Constitutions
World’s oldest ontology

5. The problem these ontologies
were built to solve
You have a lot of data / literature
The data is described in heterogeneous ways
You need to access and reason with the data
in a uniform way
1. Create a controlled vocabulary of preferred
labels for describing the data
2. Provide logical (computable) definitions
3. Tag (‘semantically enhance’) the data with
ontology term URIs

6. Ontology-based methodology of
information-driven science
Most successful example: the Gene Ontology

7. Old biology data
7

8. MKVSDRRKFEKANFDEFESALNNKNDLVHCPSITLFESIPTEVRSF
YEDEKSGLIKVVKFRTGAMDRKRSFEKVVISVMVGKNVKKFLTFV
EDEPDFQGGPISKYLIPKKINLMVYTLFQVHTLKFNRKDYDTLSLF
YLNRGYYNELSFRVLERCHEIASARPNDSSTMRTFTDFVSGAPIV
RSLQKSTIRKYGYNLAPYMFLLLHVDELSIFSAYQASLPGEKKVDT
ERLKRDLCPRKPIEIKYFSQICNDMMNKKDRLGDILHIILRACALNF
GAGPRGGAGDEEDRSITNEEPIIPSVDEHGLKVCKLRSPNTPRRL
RKTLDAVKALLVSSCACTARDLDIFDDNNGVAMWKWIKILYHEVA
QETTLKDSYRITLVPSSDGISLLAFAGPQRNVYVDDTTRRIQLYTD
YNKNGSSEPRLKTLDGLTSDYVFYFVTVLRQMQICALGNSYDAFN
HDPWMDVVGFEDPNQVTNRDISRIVLYSYMFLNTAKGCLVEYAT
FRQYMRELPKNAPQKLNFREMRQGLIALGRHCVGSRFETDLYES
ATSELMANHSVQTGRNIYGVDFSLTSVSGTTATLLQERASERWIQ
WLGLESDYHCSFSSTRNAEDVDISRIVLYSYMFLNTAKGCLVEYA
TFRQYMRELPKNAPQKLNFREMRQGLIALGRHCVGSRFETDLYE
SATSELMANHSVQTGRNIYGVDFSLTSVSGTTATLLQERASERWI
New biology data
8

9. How to do biology across the genome?
MKVSDRRKFEKANFDEFESALNNKNDLVHCPSITLFESIPTEVRSFYEDEKSGLIKVVKFRTGAMDRKRSFEKVVIS
VMVGKNVKKFLTFVEDEPDFQGGPISKYLIPKKINLMVYTLFQVHTLKFNRKDYDTLSLFYLNRGYYNELSFRVLER
CHEIASARPNDSSTMRTFTDFVSGAPIVRSLQKSTIRKYGYNLAPYMFLLLHVDELSIFSAYQASLPGEKKVDTERL
KRDLCPRKPIEIKYFSQICNDMMNKKDRLGDILHIILRACALNFGAGPRGGAGDEEDRSITNEEPIIPSVDEHGLKVC
KLRSPNTPRRLRKTLDAVKALLVSSCACTARDLDIFDDNNGVAMWKWIKILYHEVAQETTLKDSYRITLVPSSDGIS
LLAFAGPQRNVYVDDTTRRIQLYTDYNKNGSSEPRLKTLDGLTSDYVFYFVTVLRQMQICALGNSYDAFNHDPWM
DVVGFEDPNQVTNRDISRIVLYSYMFLNTAKGCLVEYATFRQYMRELPKNAPQKLNFREMRQGLIALGRHCVGSR
FETDLYESATSELMANHSVQTGRNIYGVDFSLTSVSGTTATLLQERASERWIQWLGLESDYHCSFSSTRNAEDVM
KVSDRRKFEKANFDEFESALNNKNDLVHCPSITLFESIPTEVRSFYEDEKSGLIKVVKFRTGAMDRKRSFEKVVISV
MVGKNVKKFLTFVEDEPDFQGGPISKYLIPKKINLMVYTLFQVHTLKFNRKDYDTLSLFYLNRGYYNELSFRVLERC
HEIASARPNDSSTMRTFTDFVSGAPIVRSLQKSTIRKYGYNLAPYMFLLLHVDELSIFSAYQASLPGEKKVDTERLK
RDLCPRKPIEIKYFSQICNDMMNKKDRLGDILHIILRACALNFGAGPRGGAGDEEDRSITNEEPIIPSVDEHGLKVCK
LRSPNTPRRLRKTLDAVKALLVSSCACTARDLDIFDDNNGVAMWKWIKILYHEVAQETTLKDSYRITLVPSSDGISLL
AFAGPQRNVYVDDTTRRIQLYTDYNKNGSSEPRLKTLDGLTSDYVFYFVTVLRQMQICALGNSYDAFNHDPWMD
VVGFEDPNQVTNRDISRIVLYSYMFLNTAKGCLVEYATFRQYMRELPKNAPQKLNFREMRQGLIALGRHCVGSRF
ETDLYESATSELMANHSVQTGRNIYGVDFSLTSVSGTTATLLQERASERWIQWLGLESDYHCSFSSTRNAEDVMK
VSDRRKFEKANFDEFESALNNKNDLVHCPSITLFESIPTEVRSFYEDEKSGLIKVVKFRTGAMDRKRSFEKVVISVM
VGKNVKKFLTFVEDEPDFQGGPISKYLIPKKINLMVYTLFQVHTLKFNRKDYDTLSLFYLNRGYYNELSFRVLERCH
EIASARPNDSSTMRTFTDFVSGAPIVRSLQKSTIRKYGYNLAPYMFLLLHVDELSIFSAYQASLPGEKKVDTERLKR
DLCPRKPIEIKYFSQICNDMMNKKDRLGDILHIILRACALNFGAGPRGGAGDEEDRSITNEEPIIPSVDEHGLKVCKL
RSPNTPRRLRKTLDAVKALLVSSCACTARDLDIFDDNNGVAMWKWIKILYHEVAQETTLKDSYRITLVPSSDGISLL
AFAGPQRNVYVDDTTRRIQLYTDYNKNGSSEPRLKTLDGLTSDYVFYFVTVLRQMQICALGNSYDAFNHDPWMD
VVGFEDPNQVTNRDISRIVLYSYMFLNTAKGCLVEYATFRQYMRELPKNAPQKLNFREMRQGLIALGRHCVGSRF
ETDLYESATSELMANHSVQTGRNIYGVDFSLTSVSGTTATLLQERASERWIQWLGLESDYHCSFSSTRNAEDVMK
VSDRRKFEKANFDEFESALNNKNDLVHCPSITLFESIPTEVRSFYEDEKSGLIKVVKFRTGAMDRKRSFEKVVISVM
VGKNVKKFLTFVEDEPDFQGGPISKYLIPKKINLMVYTLFQVHTLKFNRKDYDTLSLFYLNRGYYNELSFRVLERCH
EIASARPNDSSTMRTFTDFVSGAPIVRSLQKSTIRKYGYNLAPYMFLLLHVDELSIFSAYQASLPGEKKVDTERLKR
9

10. how to link the kinds of
phenomena represented here
10

11. or here
11

12. or here
12

13. MKVSDRRKFEKANFDEFESALNNKNDLVHCPSITLFESIPTEVRSFYEDEKSGLIKVVKFRTGAMDRK
RSFEKVVISVMVGKNVKKFLTFVEDEPDFQGGPIPSKYLIPKKINLMVYTLFQVHTLKFNRKDYDTLSL
FYLNRGYYNELSFRVLERCHEIASARPNDSSTMRTFTDFVSGAPIVRSLQKSTIRKYGYNLAPYMFLLL
HVDELSIFSAYQASLPGEKKVDTERLKRDLCPRKPIEIKYFSQICNDMMNKKDRLGDILHIILRACALNF
GAGPRGGAGDEEDRSITNEEPIIPSVDEHGLKVCKLRSPNTPRRLRKTLDAVKALLVSSCACTARDLD
IFDDNNGVAMWKWIKILYHEVAQETTLKDSYRITLVPSSDGISLLAFAGPQRNVYVDDTTRRIQLYTDY
NKNGSSEPRLKTLDGLTSDYVFYFVTVLRQMQICALGNSYDAFNHDPWMDVVGFEDPNQVTNRDIS
RIVLYSYMFLNTAKGCLVEYATFRQYMRELPKNAPQKLNFREMRQGLIALGRHCVGSRFETDLYESA
TSELMANHSVQTGRNIYGVDSFSLTSVSGTTATLLQERASERWIQWLGLESDYHCSFSSTRNAEDVV
AGEAASSNHHQKISRVTRKRPREPKSTNDILVAGQKLFGSSFEFRDLHQLRLCYEIYMADTPSVAVQA
PPGYGKTELFHLPLIALASKGDVEYVSFLFVPYTVLLANCMIRLGRRGCLNVAPVRNFIEEGYDGVTDL
YVGIYDDLASTNFTDRIAAWENIVECTFRTNNVKLGYLIVDEFHNFETEVYRQSQFGGITNLDFDAFEK
AIFLSGTAPEAVADAALQRIGLTGLAKKSMDINELKRSEDLSRGLSSYPTRMFNLIKEKSEVPLGHVHKI
RKKVESQPEEALKLLLALFESEPESKAIVVASTTNEVEELACSWRKYFRVVWIHGKLGAAEKVSRTKE
FVTDGSMQVLIGTKLVTEGIDIKQLMMVIMLDNRLNIIELIQGVGRLRDGGLCYLLSRKNSWAARNRKG
ELPPKEGCITEQVREFYGLESKKGKKGQHVGCCGSRTDLSADTVELIERMDRLAEKQATASMSIVAL
PSSFQESNSSDRYRKYCSSDEDSNTCIHGSANASTNASTNAITTASTNVRTNATTNASTNATTNASTN
ASTNATTNASTNATTNSSTNATTTASTNVRTSATTTASINVRTSATTTESTNSSTNATTTESTNSSTNA
TTTESTNSNTSATTTASINVRTSATTTESTNSSTSATTTASINVRTSATTTKSINSSTNATTTESTNSNT
NATTTESTNSSTNATTTESTNSSTNATTTESTNSNTSAATTESTNSNTSATTTESTNASAKEDANKDG
NAEDNRFHPVTDINKESYKRKGSQMVLLERKKLKAQFPNTSENMNVLQFLGFRSDEIKHLFLYGIDIYF
CPEGVFTQYGLCKGCQKMFELCVCWAGQKVSYRRIAWEALAVERMLRNDEEYKEYLEDIEPYHGDP
VGYLKYFSVKRREIYSQIQRNYAWYLAITRRRETISVLDSTRGKQGSQVFRMSGRQIKELYFKVWSNL
RESKTEVLQYFLNWDEKKCQEEWEAKDDTVVVEALEKGGVFQRLRSMTSAGLQGPQYVKLQFSRH
HRQLRSRYELSLGMHLRDQIALGVTPSKVPHWTAFLSMLIGLFYNKTFRQKLEYLLEQISEVWLLPHW
LDLANVEVLAADDTRVPLYMLMVAVHKELDSDDVPDGRFDILLCRDSSREVGELIGLFYNKTFRQKLE
YLLEQISEVWLLPHWLDLANVEVLAADDTRVPLYMLMVAVHKELDSDDVPDGRFDILLCRDSSREVG
ELIGLFYNKTFRQKLEYLLEQISEVWLLPHWLDLANVEVLAADDTRVPLYMLMVAVHKELDSDDVPDG
13
to this?

14. 14
or this?

15. answer: by tagging data with terms from a
controlled vocabulary such as the Gene Ontology
15
sphingolipid transporter activity
Holliday junction helicase complex
age-dependent behavioral decline

16. MouseEcotope GlyProt
DiabetInGene
GluChem
sphingolipid
transporter
activity
such tagging allows virtual integration of
heterogeneous databases
16

17. MouseEcotope GlyProt
DiabetInGene
GluChem
Holliday junction
helicase complex
17
fosters discoverability of information in
heterogeneous databases

18. Figure 3.
Shotton D, Portwin K, Klyne G, Miles A (2009) Adventures in Semantic Publishing: Exemplar Semantic Enhancements of a
Research Article. PLoS Comput Biol 5(4): e1000361. doi:10.1371/journal.pcbi.1000361
http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000361
… allows tagging of literature
RB Reis, GS Ribeiro, RDM Felzemburgh, et al., Impact of Environ-
ment and Social Gradient n Leptospira Infection in Urban Slums

19. coordinated tagging of
literature and data

20. Ontology journals
20

21. 21

22. Ontology portals
22

23. Ontology portals
23

24. Ontology authoring and editing
software
24
http://protege.stanford.edu/

25. Ontologies in domains relevant to
philosophy and cognitive science
Mental Functioning Ontology (MFO)
Ontology for Biomedical Investigations
(OBI) – philosophy of science
Basic Formal Ontology – analytic
metaphysics
Information Artifact Ontology – linguistics,
aboutness
25

26. http://bioportal.bioontology.org/ontologies/1666
Saturday, April 4, 2015 26The Emotion Ontology
with thanks to Janna
Hastings, European
Bioinformatics Institute

27. Example: Emotional personality trait
An emotional personality trait
=def. a stable enduring characteristic of a
person
which involves a predisposition (i.e. a
disposition which gives rise to an increased risk)
to undergo emotions of a particular sort, both
occurrents and dispositions.
Saturday, April 4, 2015 27

28. all terms provided with definitions
Saturday, April 4, 2015
28
The Emotion Ontology

29. Types of emotion
Saturday, April 4, 2015 29
Types of emotions

30. Types of appraisal
Saturday, April 4, 2015 30
Types of appraisal

31. Types of feeling
31

32. 32
Types of Physiological Response to Emotion

33. built by downward population from MF (which is in turn
built from BFO)
MFO-EM affective representation is_a
MFO:cognitive representation
MFO:cognitive representation is_a
BFO:specifically dependent continuant
33

34. BFO:Entity
BFO:Continuant BFO:Occurrent
BFO:Process
BFO:Independent
Continuant
BFO
MFO
BFO:Dependent
Continuant
Cognitive
Representation
Affective
Representation
Mental Process
Bodily ProcessBFO:Disposition
MFO-EM
Emotion Occurrent
Organism
Emotional Action
Tendencies
Appraisal
Subjective
Emotional Feeling
Physiological
Response to
Emotion Process
inheres_in
is_output_of
Emotional
Behavioural Process
Appraisal
Process
has_part
agent_of
Emotion Ontology Top Level

35. http://www.ifomis.org/bfo/users 35
BFO:Entity
BFO:Continuant BFO:Occurrent
BFO:ProcessBFO:Independent
Continuant
BFO:Dependent
Continuant
BFO:Disposition
To ensure the interoperability needed for data
integration, ontologies must share a common,
stable domain-neutral top level
BFO = Basic Formal Ontology

36. Anatomy Ontology
(FMA*, CARO)
Environment
Ontology
(EnvO)
Infectious
Disease
Ontology
(IDO*)
Biological
Process
Ontology (GO*)
Cell
Ontology
(CL)
Cellular
Component
Ontology
(FMA*, GO*) Phenotypic
Quality
Ontology
(PaTO)
Subcellular Anatomy Ontology (SAO)
Sequence Ontology
(SO*) Molecular
Function
(GO*)Protein Ontology
(PRO*)
Extension Strategy + Modular Organization 36
top level
mid-level
domain
level
Information Artifact
Ontology
(IAO)
Ontology for
Biomedical
Investigations
(OBI)
Spatial Ontology
(BSPO)
Basic Formal Ontology (BFO)

37. Example: biochemical basis of emotion
Emotions are effected in part by neurotransmitters
such as dopamine, tryptophan
with thanks to Janna Hastings, European Bioinformatics Institute
Saturday, April 4, 2015 37
dopamine
(CHEBI:25375)
molecular entity
(CHEBI:25375)
biological role
(CHEBI:24432)
neurotransmitter
(CHEBI:25512)
has role
neurotransmitter
receptor activity
(GO:0030594)
Molecular function
(GO:0003674)
realized in
happiness
(MFOEM:42)
part of
emotion
(MFOEM:1)
subtype

38. Is-a overloading
Toronto is a city
capital city is a city
It is a disgrace to the human race that it has
chosen to employ the same word ‘is’ for these
two entirely different ideas (predication and
identity) – a disgrace which a symbolic logic
language of course remedies. (Russell 1919:172)
38/

39. Three kinds of Relations
39
Relations between types (or ‘classes’)
is_a (= is a subtype of)
Relations between instances (or ‘individuals’)
author_of, teacher_of
Relations connecting instances to types
is_an_expert_on
is_allergic_to
is_an_instance_of

40. An ontology is a representation of types
of entities and of the relations between
them
The result of applying an ontology to a
body of data about instances is a
knowledge base
Gene Ontology (GO) vs.
Gene Ontology Annotation Database (GOA)
40

41. Manual ontology building vs. NLP
41
Natural language processing and machine
reasoning more generally are making progress
But (so far) only ontologies built by manual
experts have proven value

42. Ontology of Philosophy
42
- text vs. structured data
- conflicts of interpretation affecting the goals
of ontology itself
- no neutral perspective
- for GO and other scientific ontologies science
itself provides a neutral perspective
- what can provide the neutral perspective here?

43. Examples of philosophical
knowledge bases
43
1. Low hanging fruit,
authoritative data

44. The Philosophy Family Tree
An academic genealogy of philosophers
Only one type of link: is_Doktorvater_of
• as wiki
• as indented list
• as linked graph
140,000 entries
The largest (and longest) chain of links begins
with Leibniz
44/

45. as wiki (still working)
45/
http://philosophyfamilytree.wikispaces.com

46. 46/
http://ontology.buffalo.edu/philosophome
as indented list

47. 47/

48. http://ontology.buffalo.edu/philosophome 48
as linked graph

49. 49

50. 50/

51. 51/

52. 52/

53. Examples of philosophical
knowledge bases
53
2. Not low hanging fruit

54. With thanks to Alois Pichler
(Wittgenstein Archive, Bremen)
54
Wittgenstein Ontology
– http://wab.uib.no/cost-a32_philospace/wittgenstein.owl

55. 55
Upper Level
– http://wab.uib.no/cost-a32_philospace/wittgenstein.owl

56. Top-Level: Source
Alois Pichler (WAB). CCPL BY-
NC-SA
56

57. Top-Level: Subject
Alois Pichler (WAB). CCPL BY-
NC-SA
57

58. 58
Subject branch
• Place
– Instances: Skjolden; Cambridge
• Date
– Instances: 11 May 1936
• Issue
– Instances: philosophy; logical analysis
• Point
– Example of instance: Logical analysis is essential to philosophy
• Field (a field of philosophical discussion)
– Has subclasses:
• Epistemology
– Scepticism
» Rule-FollowingScepticism
• Perspective
– Has subclasses: APichler_Course_TLP; APichler_Course_PI
– Instances: contradiction; state_of_affairs …

59. 59
Examples of Relations
isArguedForIn
– [Philosophical analysis is essential to philosophy]
isArguedForIn [W-TLP]
isPublishedInWork
− [Ms-114,48v[5]et49r[1]] isPublishedIn [W-
PG1969:PartI:II:sect19]
isReferredToIn
– [Augustinus, Aurelius: Confessiones]
isReferredToIn [Ms-114,48v[5]et49r[1]]

60. Alois Pichler (WAB). CCPL BY-
NC-SA
60
Interlinked browsing of texts (data) and
relations (metadata)

61. Alois Pichler (WAB). CCPL BY-
NC-SA
61
Checking Wittgenstein’s references to
Augustine

62. Alois Pichler (WAB). CCPL BY-
NC-SA
62
Checking PG 1969, Part II, §17, and
focusing on one of its sources

63. 63
http://philosophyideas.com/

64. 64/

65. 65/
No controlled vocabulary

66. 66/
Mixes instances with types

67. pi
67/http://philpapers.org/

68. 68/

69. Simple ontological traffic rules
1. avoid is_a overloading
2. use exclusively singular nouns and noun
phrases
3. do not suppose that A is a kind of A & B
4. true path rule (asserting A is_a B is to assert
something that is grammatical, and
universally true)
Principal lesson of scientific ontologies:
reasoning power depends on rule 4
69/

70. 70/

71. Breaking traffic rules
• moral rationalism is_a the a priori
• the a priori is_a epistemological sources
• epistemological sources is_a epistemology
• epistemology is_a metaphysics and
epistemology
The first generation of scientific ontologies
broke these rules too. But they have learned
since then to do it right.
71/

72. Another ontological traffic rule
• Do not populate an ontology through multiple
unmonitored human sources
• Do not create an ontology on the basis of a
single source of data
– the principal value of a well-built ontology is in its
secondary uses, uses which were not anticipated
when the ontology was first developed
72/

73. PhilOnto
An example of an Ontology of
Philosophy that tries to do it right
http://ontology.buffalo.edu/philosophome/pdcp
hilontology-v1.owl
73/

74. 74

75. philosopher
75
instance_of

76. 76
Kinds and subkinds Instances

77. 77

78. philosopher
78
instance_of

79. Subkinds of philosopher
79

80. Features of PhilOnto
80
PRO
• Built on the basis of tested best practice
principles for ontology development
• Built to be extendible through an
evolutionary process
• Built manually, on the basis of careful
thinking about structure and definitions

81. Features of PhilOnto
81
CON
• Still a fragment

82. Clear distinction in InPhO between
is_a and instance_of
82
ethicist is_a [type of] philosopher
Carnap instance_of philosopher

83. 83

84. 84

85. 85

86. 86

87. InPhO Top-Level in Protégé
87
no definitions

88. InPhO Top-Level in Protégé
88
only one branch
is populated

89. InPhO second-level under ‘Idea’
89

90. “ethics is_a Idea” seems not to conform to the
expectations of statistically typical end-users
90

91. Third-level under ‘ethics’ seems
quite coherent
91

92. change is_a metaphysics
metaphysics is_a Idea
92

93. is_a and subclass
93
change is_a metaphysics
metaphysics is_a Idea
are not helped if we read ‘subclass of’ in place of
‘is_a’
since ‘subclass of’ is to be understood set-
theoretically
what would every member of the class change is
a member of the class metaphysics mean?

94. ‘instances’ in InPhO
94

95. What does ‘instance’ mean?
Colin: [it is a] kind of meaning in use, i.e., a
specification of how instances are assigned and a
contextual interpretation, supplied by end users, in
which it makes sense to say that ideas
about Japanese Zen Buddhist Philosophy are
instances of ideas about Japanese Philosophy more
generally. It is this latter, more pragmatist approach
to meaning that I prefer …

96. 6 Put more precisely, we take a computational
ontology to be a directed acyclic graph where nodes
represent concepts and the links between concepts
represent the taxonomic “isa” relation … everything
that “is a” instance of Red Wine “is a” instance of
Wine …
everything that “is a” instance of Racism “is a” instance
of African and African-American philosophy

97. Further mysteries
How is it decided what gets listed under ‘Instances’ of feminist
philosophy and what gets listed under ‘Related Terms’.
Is there any right and wrong for any of this?

98. And still further mysteries

99. Eh?

100. Features of InPhO
PRO
• Impressive tooling
• Authoritative data sources such as the Philosophy
Family Tree being used to populate the InPhO
knowledge base
• Secondary uses being explored (e.g. as part of a
robotics application to try to detect contexts in
which there are ethically significant issues in play)

101. Features of InPhO
CON
• full of mysteries
• does not follow established best practices
• no concern for interoperability with other
ontologies
• no concern for correctness of is_a hierarchies and
• no concern for logical definitions (as far as I can
see)
• thus many opportunities for reasoning with the ontology
are foreclosed

102. Challenges for InPhO
• OWL provides reasoners to check consistency
• Were inconsistencies ever found when building InPhO?
• One secondary use for ontologies is to detect errors in
databases
• Can InPhO be used to detect errors in the SEP?
• One secondary use for ontologies is to enhance existing
classification and tagging systems
• Can InPhO be used to improve the classifications in
PhilPapers?
• by finding redundancies?
• by aiding more coherent classification by identifying subsumption
relations?
• via semantic enhancement?