Towards an Ontology of Philosophy

Barry Smithhttp://ontology.buffalo.edu/smith

APA, Vancouver, April 2, 2015

World’s most successful ontology

“Siri: An Ontology-driven Application for the

Masses”, A. Cheyer and T. Gruber (2010)

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4Aristotle's Ontology of Constitutions

World’s oldest ontology

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

Ontology-based methodology of information-driven science

Most successful example: the Gene Ontology

Old biology data

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MKVSDRRKFEKANFDEFESALNNKNDLVHCPSITLFESIPTEVRSF

YEDEKSGLIKVVKFRTGAMDRKRSFEKVVISVMVGKNVKKFLTFV

EDEPDFQGGPISKYLIPKKINLMVYTLFQVHTLKFNRKDYDTLSLF

YLNRGYYNELSFRVLERCHEIASARPNDSSTMRTFTDFVSGAPIV

RSLQKSTIRKYGYNLAPYMFLLLHVDELSIFSAYQASLPGEKKVDT

ERLKRDLCPRKPIEIKYFSQICNDMMNKKDRLGDILHIILRACALNF

GAGPRGGAGDEEDRSITNEEPIIPSVDEHGLKVCKLRSPNTPRRL

RKTLDAVKALLVSSCACTARDLDIFDDNNGVAMWKWIKILYHEVA

QETTLKDSYRITLVPSSDGISLLAFAGPQRNVYVDDTTRRIQLYTD

YNKNGSSEPRLKTLDGLTSDYVFYFVTVLRQMQICALGNSYDAFN

HDPWMDVVGFEDPNQVTNRDISRIVLYSYMFLNTAKGCLVEYAT

FRQYMRELPKNAPQKLNFREMRQGLIALGRHCVGSRFETDLYES

ATSELMANHSVQTGRNIYGVDFSLTSVSGTTATLLQERASERWIQ

WLGLESDYHCSFSSTRNAEDVDISRIVLYSYMFLNTAKGCLVEYA

TFRQYMRELPKNAPQKLNFREMRQGLIALGRHCVGSRFETDLYE

SATSELMANHSVQTGRNIYGVDFSLTSVSGTTATLLQERASERWI

QWLGLESDYHCSFSSTRNAEDV

New biology data

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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

DLCPRKPIEIKYFSQICNDMMNKKDRLGDILHIILRACALNFGAGPRGGAGDEEDRSITNEEPIIPSVDEHGLKVCKL

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how to link the kinds of

phenomena represented here

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or here

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or here

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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

RFDILLCRDSSREVGE

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to this?

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or this?

answer: by tagging data with terms from a

controlled vocabulary such as the Gene Ontology

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sphingolipid transporter activity

Holliday junction helicase complex

age-dependent behavioral decline

MouseEcotope GlyProt

DiabetInGene

GluChem

sphingolipid

transporter

activity

such tagging allows virtual integration of

heterogeneous databases

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MouseEcotope GlyProt

DiabetInGene

GluChem

Holliday junction

helicase complex

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fosters discoverability of information in

heterogeneous databases

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

coordinated tagging of literature and data

Ontology journals

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Ontology portals

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Ontology portals

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Ontology authoring and editing

software

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http://protege.stanford.edu/

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

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http://bioportal.bioontology.org/ontologies/1666

Saturday, April 4, 2015 26The Emotion Ontology

with thanks to Janna Hastings, European Bioinformatics Institute

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

all terms provided with definitions

Saturday, April 4, 201528

The Emotion Ontology

Types of emotion

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Types of emotions

Types of appraisal

Saturday, April 4, 2015 30

Types of appraisal

Types of feeling

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Types of Physiological Response to Emotion

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 continuant33

BFO:Entity

BFO:Continuant BFO:Occurrent

BFO:ProcessBFO: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

http://www.ifomis.org/bfo/users35

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

Anatomy Ontology(FMA*, CARO)

Environment Ontology(EnvO)

Infectious Disease

Ontology(IDO*)

Biological Process

Ontology (GO*)

Cell Ontology

(CL)

CellularComponent

Ontology(FMA*, GO*) Phenotypic

QualityOntology

(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)

Example: biochemical basis of emotion

Emotions are effected in part by neurotransmitters such as dopamine, tryptophan

with thanks to Janna Hastings, European Bioinformatics InstituteSaturday, 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

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)

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Three kinds of Relations

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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

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)

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Manual ontology building vs. NLP

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Natural language processing and machine reasoning more generally are making progress

But (so far) only ontologies built by manual experts have proven value

Ontology of Philosophy

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- 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?

Examples of philosophical knowledge bases

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1. Low hanging fruit, authoritative data

The Philosophy Family TreeAn 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

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as wiki (still working)

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http://philosophyfamilytree.wikispaces.com

46/http://ontology.buffalo.edu/philosophome

as indented list

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http://ontology.buffalo.edu/philosophome 48

as linked graph

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Examples of philosophical knowledge bases

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2. Not low hanging fruit

With thanks to Alois Pichler

(Wittgenstein Archive, Bremen)

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Wittgenstein Ontology

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

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Upper Level

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

Top-Level: Source

Alois Pichler (WAB). CCPL BY-

NC-SA

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Top-Level: Subject

Alois Pichler (WAB). CCPL BY-

NC-SA

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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 …

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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]]

Alois Pichler (WAB). CCPL BY-

NC-SA

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Interlinked browsing of texts (data) and

relations (metadata)

Alois Pichler (WAB). CCPL BY-

NC-SA

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Checking Wittgenstein’s references to

Augustine

Alois Pichler (WAB). CCPL BY-

NC-SA

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Checking PG 1969, Part II, §17, and

focusing on one of its sources

63http://philosophyideas.com/

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No controlled vocabulary

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Mixes instances with types

pi

67/http://philpapers.org/

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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

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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.

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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

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PhilOntoAn example of an Ontology of

Philosophy that tries to do it right

http://ontology.buffalo.edu/philosophome/pdcphilontology-v1.owl

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philosopher

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instance_of

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Kinds and subkinds Instances

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philosopher

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instance_of

Subkinds of philosopher

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Features of PhilOnto

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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

Features of PhilOnto

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CON• Still a fragment

Clear distinction in InPhO between is_a and instance_of

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ethicist is_a [type of] philosopher

Carnap instance_of philosopher

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InPhO Top-Level in Protégé

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no definitions

InPhO Top-Level in Protégé

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only one branch is populated

InPhO second-level under ‘Idea’

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

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Third-level under ‘ethics’ seems quite coherent

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change is_a metaphysicsmetaphysics is_a Idea

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is_a and subclass

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change is_a metaphysicsmetaphysics 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?

‘instances’ in InPhO

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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 …

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

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?

And still further mysteries

Eh?

Features of InPhO

PRO

• Impressive tooling

• Authoritative data sources such as the Philosophy Family Tree being used to populate the InPhOknowledge 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)

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

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?