Ontologies
Hideaki Takeda

Hideaki Takeda / National Institute of Informatics

OntologiesllIntroduction of Ontology
n What is Ontology?
n Typology of Ontologies
Top-level Ontologies
n Formal Ontology
u DOLCEu BFO(Basic Formal Ontology)u Sowa’s top-level ontologyn Pragmatic top-level ontology
u SUMOu OpenCyc
n Linguistic top-level ontology
u WordNetu Penman Upper Model/The Generalized Upper Model
Hideaki Takeda / National Institute of Informatics

OntologiesllIntroduction of Ontology
n What is Ontology?
n Typology of Ontologies
Top-level Ontologies
n Formal Ontology
u DOLCEu BFO(Basic Formal Ontology)u Sowa’s top-level ontologyn Pragmatic top-level ontology
u SUMOu OpenCyc
n Linguistic top-level ontology
u WordNetu Penman Upper Model/The Generalized Upper Model
Hideaki Takeda / National Institute of Informatics

Ontology in information systeml

An ontology is an explicit specification of a conceptualization[Gruber]

Hideaki Takeda / National Institute of Informatics

Conceptualization
objecton_desk(A)on(A, B)put(A,B)box

red boxobjectbox

on_desk(A)on(A, B)put(A,B)box
color:{red, blue, yellow}

blue box yellow box

objectboxon(A/box, B/object)put(A/box,B/object)

desk
box
color:{red, blue, yellow}

There are many possible ways to conceptualize the target world
Trade off between generality and efficiency
Hideaki Takeda / National Institute of Informatics

Ontology vs. Knowledge
Knowledge in expert systems
n Descriptions of knowledge in expert systems
n The problems
u Updatingu Re-usingl The need of solid ground of knowledge for these knowledge
n Common Sense Knowledge
n OntologylHideaki Takeda / National Institute of Informatics

Ontology in Information System
An ontology is an explicit specification of a conceptualization[Gruber]l An ontology is an explicit specification of a conceptualization. The term isborrowed from philosophy, where an Ontology is a systematic account of
Existence. For AI systems, what "exists" is that which can be represented.
When the knowledge of a domain is represented in a declarative
formalism, the set of objects that can be represented is called the universeof discourse. This set of objects, and the describable relationships among
them, are reflected in the representational vocabulary with which aknowledge-based program represents knowledge. Thus, in the context ofAI, we can describe the ontology of a program by defining a set ofrepresentational terms. In such an ontology, definitions associate the namesof entities in the universe of discourse (e.g., classes, relations, functions, orother objects) with human-readable text describing what the names mean,
and formal axioms that constrain the interpretation and well-formed use of
these terms. Formally, an ontology is the statement of a logical theory.
Hideaki Takeda / National Institute of Informatics

Ontology and Agent Communication
Real World

Ontology

Knowledge？ ？

Knowledge

コミュニケーション
Hideaki Takeda / National Institute of Informatics

Types of Ontologies
Upper (top-level) ontology vs. Domain ontology
n Upper Ontology: A common ontology throughout all domains
n Domain Ontology: An ontology which is meaningful in a specificdomainl Object ontology vs. Task ontology
n Object Ontology: An ontology on “things” and “events”
n Task Ontology: An ontology on “doing”
lHideaki Takeda / National Institute of Informatics

Types of OntologieslHeavy-weight ontology vs. light-weight ontology
n Heavy-weight ontology: fully described ontology including
concept definitions and relations, in particular in a logical way
u Definition of concepts with axioms
n Light-weight ontology: partially described ontology including
typically only is-a relationsu Taxonomy
u Thesaurus

Hideaki Takeda / National Institute of Informatics

Domain ontologieslOntologies for individual domains
n UMLS (Unified Medical Language System):n Open Biomedical Ontologies (OBO)
n Gene Ontology: for genomics
n Plant Ontology: for plant structures and growth/development
stages, etc
n CIDOC CRM (Conceptual Reference Model) - an ontology for"cultural heritage information".
n …

Hideaki Takeda / National Institute of Informatics

Top-level ontology
Ontology which covers all of the world!
l Very…. Difficult
n e.g., how does a thing exist?
u A thing is four dimensional existence?
u A thing exists three-dimensionally over time?
l Common requirements
n A small number of concepts can cover the worldn Concepts can be used in lower ontologiesln Concept should be general and abstract

Hideaki Takeda / National Institute of Informatics

Top-level ontologylThree approachesn Formal approachu Logical formalizationu Fully Abstractu Pros: cleanu Cons: hardly understandable
u e.g., Sowa’s top-level ontology, DOLCE, Basic Formal Ontology (BFO)
n Pragmatic Approachu Use and extension of everyday concepts
u Mostly generalu Pros: understandable and applicable to all the worldu Cons: lack of solid foundation
u e.g. SUMO, OpenCyc, EDR
n Linguistic approachu Use and extension of linguistic concepts
u Partially abstract and partially generalu Pros: understandableu Cons: limitation to the linguistic world
u e.g., Penman Upper Model, WordNet

Hideaki Takeda / National Institute of Informatics

Formal Ontology
Formally (often logically) defined ontology
l Top-level ontology guiding or constraining domain ontologies.
ll

Most of top-level ontologies tend to be “formal”
n Formal ontology = top-level ontology

Hideaki Takeda / National Institute of Informatics

Ontological Factors
Factors are ontological grounds of the basic differencesl Categories or concepts are determined by combinations of factorsllFactorsn Sort, type (is-a, taxonomy)n Part/whole (part-of, mereology)
n Dependencen Numbern Quantity
n Spatio-temporality
n Determination, causalityn Predicablility (instance-of)
Hideaki Takeda / National Institute of Informatics

OntologiesllIntroduction of Ontology
n What is Ontology?
n Typology of Ontologies
Top-level Ontologies
n Formal Ontology
u DOLCEu BFO(Basic Formal Ontology)u Sowa’s top-level ontologyn Pragmatic top-level ontology
u SUMOu OpenCyc
n Linguistic top-level ontology
u WordNetu Penman Upper Model/The Generalized Upper Model
Hideaki Takeda / National Institute of Informatics

Formal Ontology: DOLCE
l

DOLCE(a Descriptive Ontology for Linguistic and Cognitive
Engineering)n Developed by Nicola Guarino and his associates at theLaboratory for Applied Ontology (LOA)n Intended to a reference system for top-level ontology
n Approach:u Logical definitionu Multiplicative approachn Ontology for Particulars

Hideaki Takeda / National Institute of Informatics

Particulars and UniversalslParticulars and Universals
n Particulars: cognitions of the worlds, ex., objects, qualities …
u No instancesn Universals: how concepts exist
u Instancesu Meta-ontology

Hideaki Takeda / National Institute of Informatics

DOLCE
Multiplicative approachn Different entities can be co-located in the same space-timen Combination of incompatibility of essential properties
n Aspect/rolel Ex, a vase and a mount of clayl

Hideaki Takeda / National Institute of Informatics

Formal Ontology: DOLCE
lConcepts
n Endurant / Perdurant
u Endurant (continuant)l “Things”
l An existence over timel May change its attributeu Perdurant (occurent)l “process”
l Accumulating different temporal partsl At anytime, it only partially present
l No change over timel May switch a part to the otherALL
EntityM
Amount ofMatterPED
PhysicalEndurant

F
FeaturePOB
PhysicalObjectNPED
Non- PhysicalEndurant
ED
Endurant

NPOB
Non- physicalObjectAPO
Agentive
Physical Object
NAPO
Non- agentive
Physical Object
MOB
Mental Object
SOB
Social Object

AS
Arbitrary
Sum

ACH
Achievement

EV
Event

ACC
Accomplishment

PD
Perdurant
Occurence

ST
State

STV
Stative

PRO
Process
TQ
Temporal Quality
Q
Quality

TL
Temporal Location

PQ
Physical Quality

SL
Spatial Location

AQ
Abstract Quality

AB
Abstract

Fact
Set
R
RegionTR
Temporal Region
PR
Physical Region
AR

TTime Interval
SSpace Region

Hideaki Takeda / National InstituteofRegion
Informatics
Abstract

DOLCEl

M
Amount ofMatterPED
PhysicalEndurant

Relationsn Parthood (abstract or perdurant)u “x is part of y”
u P(x, y)-> (AB(x)∨PD(x)) ∧ (AB(y)∨PD(y))

F
FeaturePOB
PhysicalObjectNPED
Non- PhysicalEndurant
ED
Endurant

NPOB
Non- physicalObjectTemporary Parthood (endurant)u “x is part of y during t”
u P(x,y,t) -> (ED(x) ∧ ED(y) ∧ T(t))
n Constitution (endurant or perdurant)u “x constitutes y during t”
u K(x, y,t)-> (AB(x)∨PD(x)) ∧ (AB(y)∨PD(y)) ∧ T(t))
n Participation (between perdurant and endurant)u “x participates in y during t”
u “PC(x,y,t) -> (ED(x) ∧PD(y) ∧T(t))
ALL
EntityAB
Abstract

MOB
Mental Object

ACH
Achievement

EV
Event

ACC
Accomplishment

PD
Perdurant
Occurence

Q
Quality

NAPO
Non- agentive
Physical Object

SOB
Social Object

AS
Arbitrary
Sum

n

APO
Agentive
Physical Object

ST
State

STV
Stative

PRO
Process
TQ
Temporal Quality

TL
Temporal Location

PQ
Physical Quality

SL
Spatial Location

AQ
Abstract Quality
Fact
Set
R
RegionTR
Temporal Region
PR
Physical Region
AR

TTime Interval
SSpace Region

Hideaki Takeda / National InstituteofRegion
Informatics
Abstract

DOLCEl

Quality and Qualen quality: the basic entity we can perceive or measureu eg., Shapes, colors, sized, sounds, smells, weight, lengths…
u Qualities inhere to entitiesl No two particulars can have the same quality
l Each quality is specifically constantly dependent on theentity it inheres inn Quales: values of qualities
u The position of an individual quality within a certain
conceptual space (quality space)u eg,. “two roses have the same color”
l Their color qualities have the same position in the color
space, ie., the same color quale
Hideaki Takeda / National Institute of Informatics

DOLCEl

PED
PhysicalEndurant

Relationsn Quality
u “x is a quality of y”
u Qt(x,y)-> Q(x) ∧(ED(y) ∨PD(y))
nMAmount ofMatterF
FeaturePOB
PhysicalObjectNPED
Non- PhysicalEndurant
ED
Endurant

NPOB
Non- physicalObjectMOB
Mental Object

ACH
Achievement

EV
Event
ALL
EntityNAPO
Non- agentive
Physical Object

SOB
Social Object

AS
Arbitrary
Sum

Quale
u “x is the quale of y during t”
u ql(x,y) -> (TR(x) ∧TQ(y))
u ql(x,y,t) -> (PR(x) ∨AR(x))∧(PQ(y) ∨AQ(y)) ∧T(t))

APO
Agentive
Physical Object

ACC
Accomplishment

PD
Perdurant
Occurence

ST
State

STV
Stative

PRO
Process
TQ
Temporal Quality
Q
Quality

TL
Temporal Location

PQ
Physical Quality

SL
Spatial Location

AQ
Abstract Quality

AB
Abstract

Fact
Set
R
RegionTR
Temporal Region
PR
Physical Region
AR

TTime Interval
SSpace Region

Hideaki Takeda / National InstituteofRegion
Informatics
Abstract

DOLCE: Formal Characterizationl

Mereological Definitions:n (Proper Part) PP(x,y)≡ P(x,y) ∧￢P(y,x)
n (overlap) O(x,y)≡∃z(P(z,x)∧P(z,y))n (Atom) At(x)≡￢∃y(PP(y,x))n (Atomic Part) AtP(x,y)≡P(x,y)∧At(x)n (Proper Part) PP(x,y,t)≡ P(x,y,t) ∧￢P(y,x,t)n (overlap) O(x,y,t)≡∃z(P(x,y,t)∧P(z,y,t)n (Atom) At(x,t)≡￢∃y(PP(y,x,t))n (Atomic Part) AtP(x,y,t)≡P(x,y,t)∧At(x,t)
n (Coincidenc) x≡ty ≡P(x,y,t)∧P(y,x,t)

Hideaki Takeda / National Institute of Informatics

DOLCE: Formal Characterization
Being presentn (Being present at t)
PRE(x,t)≡∃t’(qlT(t’, x)∧P(t,t’))n (Being present in s at t)
PRE(x,s,t)≡PRE(x,t)∧∃s’(qlS(s’, x,t)∧P(s,s’))l Perdurantn (Temporal part)PT(x,y)≡PD(x)∧P(x,y)∧∀z((P(z,y)∧z⊆Tx)→P(z,x))n (Spatial part)PS(x,y)≡PD(x)∧P(x,y)∧x ≈TylHideaki Takeda / National Institute of Informatics

DOLCE: Formal CharacterizationlParticipationn (Constant Participation)
PCC (x,y)≡∃t(PRE(y,t))∧∀t(PRE(y,t)→PC(x,y,t))n (Temporary Total Participation)
PCT (x,y,t)≡PD(y)∧∀z((P(z,y)∧PRE(z,t))→PC(x,z,t))n (Total Participation)
PCT(x,y)≡∃t(qlT(t, y)∧PCT(x,y,t))

Hideaki Takeda / National Institute of Informatics

Particulars and UniversalslParticulars and Universals
n Particulars: cognitions of the worlds, ex., objects, qualities …
u No instancesn Universals: how concepts exist
u Instancesu Meta-ontology

Hideaki Takeda / National Institute of Informatics

Meta ontologylOntology for ontology
n Only abstract aspectu Ontology for Universals
l Attribute of attribute

Hideaki Takeda / National Institute of Informatics

Some properties on predicates
Difference between rigid and non-rigid predicates (rigidity)n Human-being vs. teacherl Difference between divisive and non-divisive predicates (divisivity)n Apple vs. waterl Difference between identical and non-identical predicatesl …
lHideaki Takeda / National Institute of Informatics

Rigidity
Rigidity (+R): an essential property for beingn If the entity looses it, it cannot exist anymoren Ex., “being a human”
l Non-rigid (-R): an property which is essential to some entities andnot to othersn Ex. “having a brain” in Wizard of Oz
u Scarecrow does not have a brainn Anti-rigid (~R): it cannot be essential to any entities
u Ex. “being a student”
l Subsumption constraintn Anti-rigid properties cannot subsume rigid propertieslHideaki Takeda / National Institute of Informatics

Identity
Identity: properties to recognize individual entities as being the samel Identity criteria: conditions to determine equality (sufficientconditions) and that entailed by equality (necessary conditions)n Identity over time?
n Two entities are the same?
l E.g.,n Time duration (e.g., “one hour”) and Time interval (e.g., “9:0010:00 May 10, 2009”)
u Time duration subsumes Time interval?
u Time interval have Time duration?
n A clay statue can be identical if the shape is not changedl Identity is sometimes inherited to subsumed entities and some notn Identity (+I)
n Own (+O): Can not be inherited
Hideaki Takeda / National Institute of InformaticslUnityUnity: being able to recognize all the parts that form an individual
entityn E.g.,
u “being water”
u “being an ocean”
l Unity criteria: conditions that must hold among the parts of a certain
entity in order to consider it a wholen Topological wholes (a piece of coal)n Morphological whole (a constellation)n Functional whole (a hammer)l Unity (+U), non-unity (-U), anti-unity (~U)
lHideaki Takeda / National Institute of Informatics

Formalization
PP(x,y,t)=def f(x,y,t) ∧￢x=y (proper part)l O(x,y,t) =def ∃z(P(z,x,t)∧P(z,y,t)) (overlap)l P(x,y,t) → E(x,t) ∧ E(y,t) (actual existence of parts)l P(x,y,t)∧P(y,x,t) → x=y (antisymmetry)l P(x,y,t)∧P(y,z,t)→P(x,z,t) (transitivity)l PP(x,y,t)→∃z(PP(z,y,t)∧￢O(z,x,t)) (weak supplementation)l

Hideaki Takeda / National Institute of Informatics

Formalizationl

Rigidityn Rigid Φ+R: Φ is a necessary property for all its instancesn Non-Rigid Φ-R: Φ is not a necessary property for all its instancesn Anti-Rigid Φ~R: Φ is an optional property for all its instances

Hideaki Takeda / National Institute of Informatics

IdentitylΦ(x) ∧ Φ(y) → (ρ(x, y) ⇔ x=y)lDef. 1: Let Φ be a rigid property, and Γ(x,y,t,t’) a formula containing
x,y,t,t’ as the only free variable such that￢∀xytt’(Γ(x,y,t,t’)⇔x=y)
We say that Φ carries the IC Γ iff one of the following conditions isverifiedn Def. 2 Γ is a necessary IC carried by Φ when:E(x,t)∧Φ(x,t)∧E(y,t’)∧Φ(y,t’)∧x=y→Γ(x,y,t,t’)￢∀xy(E(x,t)∧Φ(x,t)∧E(y,t’)∧Φ(y,t’)∧xy→Γ(x,y,t,t’))n Def. 3 Γ is a sufficient IC carried byΦ when:E(x,t)∧Φ(x,t)∧E(y,t’)∧Φ(y,t’)∧ Γ(x,y,t,t’) → x=y∃xytt’Γ(x,y,t,t’)Hideaki Takeda / National Institute of Informatics

l
l
l
l
l

l

Ics are “inherited” along a hierarchy of propertiesif Φ(x) →φ(x) and Γ is a necessary IC for φ then Γ is a necessary IC for ΦDef. 4: A non-rigid property carries an IC Γ iif it is subsumed by a rigid property
carrying ΓDef. 5: Any property carrying an IC is marked with the meta property +I (-I
otherwise)
Def. 6: A property Φ supplies an IC Γ iff i) it is rigid; ii) it carries Γ; and iii) Γis not caries by all the properties subsuming ΦDef. 7: Any property supplying an IC is marked with the meta property +O(-O
otherwise).
n +O implies +I and +R
n E.g., Person and student
Def. 8: Any property carrying an IC (+I) is called a sortal

Hideaki Takeda / National Institute of Informatics

UnityDef. 9: Let ω be an equivalence relation. At a given time t, An object
x is contingent whole under ω if:￢(P(y,x,t)∧P(z,x,t) ⇔ ω(z,y,t))∀y(P(y,x,t)→∀z(P(z,x,t)⇔ω(z,y,t)))“at time t, each part of x must be bound by ω to all other parts and tonothing else”
l Variety of Unity Conditionn Topological unity: based on some kind of topological conncetions(a piece of coal, a lump of coal)n Morphological unity: based on shape (a ball, a constellation)n Functional unity: (a hammer, a bikini)lHideaki Takeda / National Institute of Informatics

Def. 10: Let ω be an equivalence relation. An object x is an intrinsic
whole under ω if, at any time where x exists, it is a contingent whole
under ωl Def. 11: A property Φ carries a unity condition (+U) iff there exists
an equivalence relation ω such that all its instances are intrinsic
wholes under ωlHideaki Takeda / National Institute of Informatics

Dependence (+D)
l

Property Φ is externally dependent on a property Ψ if, for all its
instances x, necessarily some instance of Ψ must exist, which is not apart nor a constituent of x.
n E.g., parent depends child

Hideaki Takeda / National Institute of Informatics

Constraints
Rigidity constraintsn Φ~R can’t subsume Ψ+R
l Identity constraints
n Φ+I can’t subsume Ψ-I
l Unity constraints
n Φ+U can’t subsume Ψ-Un Φ-U can’t subsume Ψ+Ul Dependence constraints
n Φ+D can’t subsume Ψ-Dl (Identity and Own Identity)
n +O → +I
n +O → +Rl

Hideaki Takeda / National Institute of Informatics

Constraintslq subsumes p, then the following constraints holdn If q is anti-rigid (~R), then p must be anti-rigid (~R)
n If q carries an identity criterion (+I), then p must carry the same
criterion (+I)
n If q caries a unity criterion (+U), then p must carry the same
criterion (+U)
n If q has anti-unity (~U), then p must also have anti-unity (~U)

Hideaki Takeda / National Institute of Informatics

Meta ontologylAttribute of attributen Rigidity (R)Category (+R)
Non-sortal (-I)

Property

Formal Role

Role (~R+D)
Anti-Rigid (~R)

n Identity (I)n Own Identity (O)Attribution (-R-D)

Non-rigid (-R)

Material Role
Phased Sortal (-D)
Mixin (-D)

Sortal (+I)

Type (+O)

n Dependency (D)Rigid (+R)

Quasi-Type (-O)

n +: should be satisfied
n -: is not satisfied
n ~: each attribute is not satisfied

Hideaki Takeda / National Institute of Informatics

An example

Hideaki Takeda / National Institute of Informatics

Ontological checking
Entity: -I-U+R
l Location: +O~U+R
l Amount of matter: +O~U+R
l Red: -I-U-R
l Agent: -I-U~R
l Group; +O~U+R
l Physical object: +O+U+R
l Living Being: +O+U+R
l Food: +I~U~R
l Animal: +O+U+Rl

Legal Agent: +O-U~R
l Group of People: +I~U+R
l Social Entity: -I+U+R
l Organization; +O+U+R
l Fruit: +O+U+R
l Apple: +O+U+R
l Red Apple: +O+U~R
l Vertebrate: +I+U+R
l Person: +O+U+R
l Butterfly and Caterpillar:+I+U~Rl Country: +O+U~Rl

Hideaki Takeda / National Institute of Informatics

〜U(AMOUNT-OF-MATTER) .cannot subsume +U(PHYSICAL-OBJECT)
Hideaki Takeda / National Institute of Informatics

Category (+R)
Non-sortal (-I)

Ontology checking
Property

Attribution (-R-D)
Formal Role

Role (~R+D)
Anti-Rigid (~R)
Non-rigid (-R)

Material Role
Phased Sortal (-D)
Mixin (-D)

Sortal (+I)

Type (+O)
Rigid (+R)

Quasi-Type (-O)

Hideaki Takeda / National Institute of Informatics

Hideaki Takeda / National Institute of Informatics

Agent and legal agent+D(AGENT) cannot subsume ‒D(ANIMAL)
Hideaki Takeda / National Institute of Informatics

Hideaki Takeda / National Institute of Informatics

Ontology checking

Hideaki Takeda / National Institute of Informatics

OntologiesllIntroduction of Ontology
n What is Ontology?
n Typology of Ontologies
Top-level Ontologies
n Formal Ontology
u DOLCEu BFO(Basic Formal Ontology)u Sowa’s top-level ontologyn Pragmatic top-level ontology
u SUMOu OpenCyc
n Linguistic top-level ontology
u WordNetu Penman Upper Model/The Generalized Upper Model
Hideaki Takeda / National Institute of Informatics

Basic Formal Ontology
Barry Smith and his colleagues at the Institute for Formal Ontology
and Medical Information Science (IFOMIS) at the University ofLeipzig and in University of Buffalol Basically developed for biomedicine applicationsn To represent the biological reality
u The static or the dynamic aspect of the biological realityl

Hideaki Takeda / National Institute of Informatics

Methodology
Realism (実在論) holds thatn Reality and its constituents exist independently of our (linguistic,
conceptual, theoretical, cultural) representations thereofl Fallibilism (可謬主義) accepts thatn Our theories and classifications can be subject to revisionl Perspectivalism (観点主義) maintains thatn There exists a plurality of alternative, equally legitimate
perspectives on reality
l Adequatism (十全主義) maintains thatn These alternative views are not reducible to any single basic
views. Thus adequatism is opposed to reductionism, i.e., to thethesis that there is some one privileged perspective to which allother representaions of reality can be reducedl

加地 大介、オントロジー構築のための実在論的方法論、『人工知能学会誌
20 巻5Institute号 (2005 of
年9Informatics
月)』, 595－603 頁
HideakiTakeda / National

Basic Formal Ontology
Two dimensionsn Opposition between different levels of granularity
n Opposition between objects and processesl Continuants and Occurrents
n Continuants
u bound with respect to spacen Occurentsu bound with respect to timeu Can have temporal parts or phaseslHideaki Takeda / National Institute of Informatics

Basic Formal Ontology
SNAP ontology and SPAN ontology
n SNAP Ontology for continuantsn SPAN Ontology for Occurents
n Relations between continuants and occurrents are inter-ontologial
l Opposed to three-dimensionalists and four-dimensionalists
n Three-dimensionalists
u Process of change and motion are not entities but terms ofsequences of attributes of continuant entities
n Four-dimensionalists
u Entities are like “spatio-temporal worms” or “process-thing”
n Both do not accept the other entitieslHideaki Takeda / National Institute of Informatics

SNAP Ontology• Substances• Maximal connected substantial
entities• Do not depend for other being• Bearers of qualities• Preserve identity overtime and through changes ofvarious sorts
SNAP
entities• Have a location in space• Self-connected wholes which
physical bourndaries

Substances
Fiat Parts
Substantial
entities

Boundaries
Aggregates ofsubstances
Sites
Qualities

SNAP
dependent
entities

Roles
Functions…
PointsSpatialregions

Lines
Surfaces
Volumes

Hideaki Takeda / National Institute of Informatics

Nichesetc.

SNAP Ontology••

Fiat Parts (規約的部分)• E.g., lower lobes of the left lung
(左肺下葉）• Not demarcated by physical discontinuities
Boundaries• Lower dimensional parts of spatial entities• E.g., the inner surface of the stomach
SNAP•fiat vs. bona fide (真性)entities

Substances
Fiat Parts
Substantial
entities

Boundaries
Aggregates ofsubstances
Sites
Qualities

SNAP
dependent
entities

Roles
Functions…
PointsSpatialregions

Lines
Surfaces
Volumes

Hideaki Takeda / National Institute of Informatics

Nichesetc.

••Aggregates• Mereological sums comprehending
separate substances as parts• Non connected boundaries
Sites• Holes, cavities and so on• E.g., spinal canal (脊髄管), cranial
cavity (頭蓋腔), the lumen of thegastrointestinal tract (胃腸内管)

Substances
Fiat Parts
Substantial
entities

Boundaries
Aggregates ofsubstances
Sites
Qualities

SNAP
entities

SNAPdependent
entities

Roles
Functions…
PointsSpatialregions

Lines
Surfaces
Volumes

Hideaki Takeda / National Institute of Informatics

Nichesetc.

SNAP Ontology
Substances•

SNAP Dependent Entities• Entities which endure in time andwhich inhere in substantial entities• “Inherence” is an intra-ontologicalrelation
between a SNAP dependent entity
and its substantial bearer• E.g., the redness of the red blood,the shape of a nose
• Monadic: qualities• Polyadic or relational: connection

Fiat Parts
Substantial
entities

Boundaries
Aggregates ofsubstances
Sites
Qualities

SNAP
entities

SNAPdependent
entities

Roles
Functions…
PointsSpatialregions

Lines
Surfaces
Volumes

Hideaki Takeda / National Institute of Informatics

Nichesetc.

SNAP Ontology•

SNAP Universals• Universals form taxonomies of various sorts• Each taxonomy is a tree
• Greater and lesser generality• Substantial universals
and universals instantiated by SNAP dependent
entities
SNAP
entities

Substances
Fiat Parts
Substantial
entities

Boundaries
Aggregates ofsubstances
Sites
Qualities

SNAP
dependent
entities

Roles
Functions…
PointsSpatialregions

Lines
Surfaces
Volumes

Hideaki Takeda / National Institute of Informatics

Nichesetc.

SPAN
entities

Processual
entities

Fiat Parts

SPAN Ontology•

Process• the natural units in the processualrealm• Have bona fide beginnings and endings
corresponding to real discontinuities in
the processual order.
• E.g., the life of an organsim, theprocess of sleeping (the boundary offalling asleep and waking up)• Be maximal spatiotemprally connected
wholesProcesses

Aggregates

TemporalRegions

Spatiotemporal
regions

Instantaneous
temporalboundaries

EventsConnected

Interval

Scattered

Instant

Connected
Scattered

Hideaki Takeda / National Institute of Informatics

SPAN
entities

Processual
entities

Fiat Parts

SNAN Ontology•

Fiat parts• The parts of processes on a given levelof granularity are by definition fiat
parts
• E.g., an individual chewing process inthe course of a meal

Processes

Aggregates

TemporalRegions

Spatiotemporal
regions

Instantaneous
temporalboundaries

EventsConnected

Interval

Scattered

Instant

Connected
Scattered

Hideaki Takeda / National Institute of Informatics

SPAN
entities

Processual
entities

Fiat Parts

SNAN Ontology••

Events• Instantaneous temporal boundaries ofprocesses• Fiat and bona fide
• E.g., forming of a synapse, the onset of
REM sleep
Aggregates of processes• E.g., the aggregate of all neuronalfiring involved in the generation of athought

Processes

Aggregates

TemporalRegions

Spatiotemporal
regions

Instantaneous
temporalboundaries

EventsConnected

Interval

Scattered

Instant

Connected
Scattered

Hideaki Takeda / National Institute of Informatics

OntologiesllIntroduction of Ontology
n What is Ontology?
n Typology of Ontologies
Top-level Ontologies
n Formal Ontology
u DOLCEu BFO(Basic Formal Ontology)u Sowa’s top-level ontologyn Pragmatic top-level ontology
u SUMOu OpenCyc
n Linguistic top-level ontology
u WordNetu Penman Upper Model/The Generalized Upper Model
Hideaki Takeda / National Institute of Informatics

Formal Ontology
Sowa’s top-level ontology
n Physical / Abstractn Independent / Relative / Mediatingn Continuant / Occurrent^lPhysicalActuality

Form

MediatingRelative

Independent

Abstract

Prehension Proposition Nexus

Intention
Occurrent

Continuant

ObjectSchemaProcess

Juncture
ScriptDescription

Participation

Structure

History

ReasonSituation^

Hideaki Takeda / National Institute of Informatics

PurposeOntologiesllIntroduction of Ontology
n What is Ontology?
n Typology of Ontologies
Top-level Ontologies
n Formal Ontology
u DOLCEu BFO(Basic Formal Ontology)u Sowa’s top-level ontologyn Pragmatic top-level ontology
u SUMOu OpenCyc
n Linguistic top-level ontology
u WordNetu Penman Upper Model/The Generalized Upper Model
Hideaki Takeda / National Institute of Informatics

Pragmatic top-level ontology

Ｓｕｂｓｔａｎｃｅ

ＳｅｌｆＣｏｎｎｅｃｔｅｄＯｂｊｅｃｔ

Ｏｒｇａｎｉｃ

ＣｏｒｐｕｓｃｕｌａｒＯｂｊｅｃｔ

Ｉｎｏｒｇａｎｉｃ

Ｏｂｊｅｃｔl

SUMO(Suggested
Upper Merged
Ontology)n Collection andorganization ofconcepts usedfrequently Entity
n Simplerelationshipbetweenconcepts

Ｃｏｌｌｅｃｔｉｏｎ
ＰｈｓｉｃａｌＮａｔｕｒａｌＰｒｏｃｅｓｓ

Ｐｒｏｃｅｓｓ

Ｉｎｔｅｎｔｉｏｎａｌｌｙ
ＣａｕｓｅｄＰｒｏｃｅｓｓＢｉｏｌｏｇｉｃａｌ
Ｐｒｏｃｅｓｓ

Ｐｈｉｓｉｏｌｏｇｉｃ
ＰｒｏｃｅｓｓＰａｔｈｏｊｏｇｉｃ
ＰｒｏｃｅｓｓＣｈａｎｇｅＯｆＰｒｏｓｓｅｓｓｉｏｎ
Ｓｅａｒｃｈｉｎｇ

Ｃｏｍｍｕｎｉｃａｔｉｏｎ

ＳｏｃｉａｌＩｎｔｅｒａｃｔｉｏｎ

Ｃｏｏｐｅｒａｔｉｏｎ
Ｃｏｎｔｅｓｔ
Ｍｅｅｔｉｎｇ

Transfer

ＭｏｔｉｏｎImpacting
BringingTogether

Abstract

ＣｈａｎｇｅＯｆ
State

Separating

Impelling
Putting
Removing
Transportation

Hideaki Takeda / National Institute of Informatics

Pragmatic top-level ontologylOpenCycn Cyc: A project to construct very-large common knowledge basen OpenCyc: top-level concepts in Cyc knowledge baseu Cyc: 120, 000 concepts and their relationsu OpenCyc: 6,000 concepts and their relations#$Thing
n Concepts are defined logically#$Individual• #$Intangible#$TemporalThing#$SpatialThing-Localized#$PartiallyTangible#$SetOrCollection#$Event#$Collection#$ExistingStuffType#$genls#$typeGenls#$disjointWith#$ExistingObjectType

Hideaki Takeda / National Institute of Informatics

Pragmatic top-level ontologylOpenCycn Basic relations
u #$genls: set inclusionl Hierarchy of categories is set inclusion
u #$isa: relationship between a class and its instancel E.g,(#$isa #$Dog #$BiologicalSpecies)n (#$genls #$Dog #$Carnivore)n An instance of #$Dog is also an instance of #$Carnivore but not an
instance of #$BiologicalSpeiciesnu Logical operators like #$and and #$disjointWithu Specific operators like #$biologicalRelatives

Hideaki Takeda / National Institute of Informatics

OntologiesllIntroduction of Ontology
n What is Ontology?
n Typology of Ontologies
Top-level Ontologies
n Formal Ontology
u DOLCEu BFO(Basic Formal Ontology)u Sowa’s top-level ontologyn Pragmatic top-level ontology
u SUMOu OpenCyc
n Linguistic top-level ontology
u WordNetu Penman Upper Model/The Generalized Upper Model
Hideaki Takeda / National Institute of Informatics

Linguistic top-level ontology
Lexicon, thesaurus and dictionaries are close to ontologyn Plenty of concepts and their relationsn Lack of formal systeml Difference between linguistic knowledge and object knowedge
n Linguistic knowledge: grammar, categories …
n Object knowledge: knowledge about target (ontology)n Two knowledge are dependent to each otherl WordNetl Penman Upper Model/The Generalized Upper Modell

Hideaki Takeda / National Institute of Informatics

OntologiesllIntroduction of Ontology
n What is Ontology?
n Typology of Ontologies
Top-level Ontologies
n Formal Ontology
u DOLCEu BFO(Basic Formal Ontology)u Sowa’s top-level ontologyn Pragmatic top-level ontology
u SUMOu OpenCyc
n Linguistic top-level ontology
u WordNetu Penman Upper Model/The Generalized Upper Model
Hideaki Takeda / National Institute of Informatics

WordNetl

A lexical reference systemn “Link-based electronic dictionary”
http://www.cogsci.princeton.edu/cgi-bin/webwnlPos

Unique Strings

Synsets

Word-Sense Pairs

Noun117,798

83,115

146,312

Verb

11,52912,76725,047

Adjective

21,47918,15630,002Adverb

4,481

3,621

5,580

Total

155,287

117,659

206,941

Synset Relations
n synonym
n hypernym/hyponym (is-a)n holonym/meronym (part-of)
Hideaki Takeda / National Institute of Informatics

WordNet Synset
wordroom (n)polysemysynsetS: (n) room (an area within a building enclosed by walls and floor
and ceiling) "the rooms were very small but they had a nice view"
S: (n) room, way, elbow room (space for movement) "room topass"; "make way for"; "hardly enough elbow room to turn around"
S: (n) room (opportunity for) "room for improvement"
S: (n) room (the people who are present in a room) "the whole room
was cheering"

Hideaki Takeda / National Institute of Informatics

S: (n) entity (that which is perceived or known or inferredto have its own distinct existence (living or nonliving))
S: (n) physical entity (an entity that has physical existence)
S: (n) object, physical object (a tangible and visible
entity; an entity that can cast a shadow)
S: (n) whole, unit (an assemblage of parts that isregarded as a single entity)

hypersym(is-a)

S: (n) artifact, artefact (a man-made object taken as a whole)
S: (n) structure, construction (a thing constructed; acomplex entity constructed of many parts)

hypersym(is-a)

hypersym(is-a)hyposym(is-a)

S: (n) area (a part of a structure having some specificcharacteristic or function)
S: (n) room (an area within a building
enclosed by walls and floor and ceiling)

hyposym(is-a)
S: (n) anechoic
chamber (a chamberhaving very little
reverberation)

S: (n) anteroom,
antechamber, entrance hall,
hall, foyer, lobby, vestibule (alarge entrance or reception
room or area)

S: (n) back room (a room locatedin the rear of an establishment;
usually accessible only toprivileged groups)

S: (n) …

Hideaki Takeda / National Institute of Informatics

S: (n) building, edifice (a structure that has a roof andwalls and stands more or less permanently in one place)holonym (part-of)
S: (n) room (an area within a building
enclosed by walls and floor and ceiling)meronym (part-of)

S: (n) ceiling (the overhead upper surface of acovered space)
S: (n) floor, flooring (the inside lower horizontal
surface (as of a room, hallway, tent, or otherstructure))
S: (n) room light (light that provides general
illumination for a room)
S: (n) wall (an architectural partition with a height
and length greater than its thickness; used to divide
or enclose an area or to support another structure)

Hideaki Takeda / National Institute of Informatics

S: (n) building, edifice (a structure that has a roof andwalls and stands more or less permanently in one place)meronym (part-of)

S: (n) annex, annexe, extension, wing (an addition that extends a mainbuilding)
S: (n) anteroom, antechamber, entrance hall, hall, foyer, lobby,
vestibule (a large entrance or reception room or area)
S: (n) corner, quoin ((architecture) solid exterior angle of a building;especially one formed by a cornerstone)
S: (n) room (an area within a building
enclosed by walls and floor and ceiling)

Instance-of
Independence Hall
Houses of Parliament

Hideaki Takeda / National Institute of Informatics

WordNetl

Top-level (note: WordNet 2.1)
n { entity, physical thing (that which is perceived or known or inferred to haveits own physical existence (living or nonliving)) }n { psychological_feature, (a feature of the mental life of a living organism) }n { abstraction, (a general concept formed by extracting common features from
specific examples) }n { state, (the way something is with respect to its main attributes; "the current
state of knowledge"; "his state of health"; "in a weak financial state") }n { event, (something that happens at a given place and time) }n { act, human_action, human_activity, (something that people do or cause tohappen) }n { group, grouping, (any number of entities (members) considered as a unit) }n { possession, (anything owned or possessed) }n { phenomenon, (any state or process known through the senses rather than by
intuition or reasoning) }Hideaki Takeda / National Institute of Informatics

WordNet Synset
S: (n) entity (that which is perceived or known or inferred tohave its own distinct existence (living or nonliving))
S: (n) physical entity (an entity that has physicalexistence)

S: (n) thing (a separate and self-contained entity)
S: (n) object, physical object (a tangible and visible entity;
an entity that can cast a shadow)
S: (n) causal agent, cause, causal agency (any entity that
produces an effect or is responsible for events or results)
S: (n) matter (that which has mass and occupies space)
S: (n) process, physical process (a sustained phenomenon
or one marked by gradual changes through a series ofstates)S: (n) substance (material of a particular kind or
constitution)

S: (n) abstraction, abstract entity (a general
concept formed by extracting common featuresfrom specific examples)
S: (n) thing (an entity that is not named
specifically)

Hideaki Takeda / National Institute of Informatics

WordNet Synset
S: (n) thing (a separate and self-contained entity)

S: (n) subject, content, depicted object (something (a person or object or scene)
selected by an artist or photographer for graphic representation) "a moving pictureof a train is more dramatic than a still picture of the same subject"
S: (n) body of water, water (the part of the earth's surface covered with water (suchas a river or lake or ocean)) "they invaded our territorial waters"; "they weresitting by the water's edge"
S: (n) inessential, nonessential (anything that is not essential) "they discarded alltheir inessentials"
S: (n) necessity, essential, requirement, requisite, necessary (anything
indispensable) "food and shelter are necessities of life"; "the essentials of the goodlife"; "allow farmers to buy their requirements under favorable conditions"; "aplace where the requisites of water fuel and fodder can be obtained"
S: (n) part, piece (a portion of a natural object) "they analyzed the river into threeparts"; "he needed a piece of granite"
S: (n) reservoir, source (anything (a person or animal or plant or substance) inwhich an infectious agent normally lives and multiplies) "an infectious agent
depends on a reservoir for its survival"
S: (n) unit, building block (a single undivided natural thing occurring in thecomposition of something else) "units of nucleic acids"
S: (n) variable (something that is likely to vary; something that is subject tovariation) "the weather is one variable to be considered"
Hideaki Takeda / National Institute of Informatics

WordNet Synset
S: (n) object, physical object (a tangible and visible entity;
an entity that can cast a shadow)

S: (n) whole, unit (an assemblage of parts that is regarded as a single entity
S: (n) location (a point or extent in space)
S: (n) charm, good luck charm (something believed to bring good luck)
S: (n) curio, curiosity, oddity, oddment, peculiarity, rarity (something unusual -- perhaps worthy of collecting)
S: (n) draw, lot (anything (straws or pebbles etc.) taken or chosen at random)
S: (n) film (a thin coating or layer)
S: (n) hoodoo (something believed to bring bad luck)
S: (n) je ne sais quoi (something indescribable)
S: (n) keepsake, souvenir, token, relic (something of sentimental value)
S: (n) makeweight, filler (anything added to fill out a whole)
S: (n) part, portion (something less than the whole of a human artifact)
S: (n) property, prop (any movable articles or objects used on the set of a play or movie)
S: (n) snake (something long, thin, and flexible that resembles a snake)
S: (n) stuff (miscellaneous unspecified objects)
S: (n) triviality, trivia, trifle, small beer (something of small importance)
S: (n) paring ((usually plural) a part of a fruit or vegetable that is pared or cut off; especially the skin or peel)
S: (n) catch (anything that is caught (especially if it is worth catching))
S: (n) commemorative (an object (such as a coin or postage stamp) made to mark an event or honor a person)
S: (n) discard (anything that is cast aside or discarded)

Hideaki Takeda / National Institute of Informatics

WordNet Synset
S: (n) object, physical object (a tangible and visible entity;
an entity that can cast a shadow)
S: (n) finding (something that is found)
S: (n) floater (an object that floats or is capable of floating)
S: (n) fomite, vehicle (any inanimate object (as a towel or money or clothing or dishes or books or toysetc.) that can transmit infectious agents from one person to another)
S: (n) geological formation, formation ((geology) the geological features of the earth)
S: (n) growth (something grown or growing)
S: (n) hail (many objects thrown forcefully through the air)
S: (n) head (a rounded compact mass)
S: (n) ice (the frozen part of a body of water)
S: (n) land, dry land, earth, ground, solid ground, terra firma (the solid part of the earth's surface)
S: (n) land, ground, soil (material in the top layer of the surface of the earth in which plants can grow(especially with reference to its quality or use))
S: (n) moon (any object resembling a moon)
S: (n) neighbor, neighbour (a nearby object of the same kind)
S: (n) remains (any object that is left unused or still extant)
S: (n) ribbon, thread (any long object resembling a thin line)
S: (n) shiner (something that shines (with emitted or reflected light))
S: (n) vagabond (anything that resembles a vagabond in having no fixed place)
S: (n) wall (anything that suggests a wall in structure or function or effect)
S: (n) web (an intricate network suggesting something that was formed by weaving or interweaving

Hideaki Takeda / National Institute of Informatics

WordNet Synset
S: (n) causal agent, cause, causal agency (any entity that
produces an effect or is responsible for events or results)
S: (n) person, individual, someone, somebody, mortal, soul (a human being)
S: (n) agent (an active and efficient cause; capable of producing a certain effect)
S: (n) nature (a causal agent creating and controlling things in the universe)
S: (n) supernatural, occult (supernatural forces and events and beings collectively)
S: (n) theurgy (the effect of supernatural or divine intervention in human affairs)
S: (n) first cause, prime mover, primum mobile (an agent that is the cause of all things
but does not itself have a cause) "God is the first cause"
S: (n) destiny, fate (the ultimate agency regarded as predetermining the course of events(often personified as a woman)) "we are helpless in the face of destiny"
S: (n) catalyst (something that causes an important event to happen)
S: (n) deus ex machina (any active agent who appears unexpectedly to solve an
insoluble difficulty)
S: (n) operator, manipulator (an agent that operates some apparatus or machine)
S: (n) power, force (one possessing or exercising power or influence or authority)
S: (n) vital principle, life principle (a hypothetical force to which the functions andqualities peculiar to living things are sometimes ascribed)
S: (n) engine (something used to achieve a purpose) "an engine of change"
S: (n) cause of death, killer (the causal agent resulting in death)
S: (n) danger (a cause of pain or injury or loss)
S: (n) agent (a substance that exerts some force or effect)

Hideaki Takeda / National Institute of Informatics

WordNet Synset
S: (n) matter (that which has mass and occupies space)
S: (n) substance (the real physical matter of which a person or thing consists)
S: (n) substance (a particular kind or species of matter with uniform properties)
S: (n) sediment, deposit (matter that has been deposited by some natural process)S: (n) ylem ((cosmology) the original matter that (according to the big bang theory) existed before the formation of
the chemical elements)
S: (n) dark matter ((cosmology) a hypothetical form of matter that is believed to make up 90 percent of theuniverse; it is invisible (does not absorb or emit light) and does not collide with atomic particles but exerts
gravitational force)
S: (n) antimatter (matter consisting of elementary particles that are the antiparticles of those making up normal
substances)
S: (n) glop (any gummy shapeless matter; usually unpleasant)S: (n) fluid (continuous amorphous matter that tends to flow and to conform to the outline of its container: a liquid
or a gas)
S: (n) sludge, slime, goo, goop, gook, guck, gunk, muck, ooze (any thick, viscous matter)
S: (n) system ((physical chemistry) a sample of matter in which substances in different phases are in equilibrium)
S: (n) residue (matter that remains after something has been removed)
S: (n) solid (matter that is solid at room temperature and pressure)
S: (n) solute (the dissolved matter in a solution; the component of a solution that changes its state)
S: (n) emanation (something that is emitted or radiated (as a gas or an odor or a light, etc.))
S: (n) vegetable matter (matter produced by plants or growing in the manner of a plant)
Hideaki Takeda / National Institute of Informatics

WordNet Synset

S: (n) process, physical process (a sustained phenomenon or
one marked by gradual changes through a series of states)

S: (n) phenomenon (any state or process known through the senses rather than by intuition or reasoning)
S: (n) chelation ((medicine) the process of removing a heavy metal from the bloodstream by means of a chelate as in treating lead or
mercury poisoning)
S: (n) dealignment (a process whereby voters are moved toward nonpartisanship thus weakening the structure of political parties)
S: (n) decrease, decrement (a process of becoming smaller or shorter)
S: (n) degeneration, devolution (the process of declining from a higher to a lower level of effective power or vitality or essential quality)
S: (n) development, evolution (a process in which something passes by degrees to a different stage (especially a more advanced or mature
stage)) "S: (n) economic process (any process affecting the production and development and management of material wealth)
S: (n) encapsulation (the process of enclosing (as in a capsule))
S: (n) execution, instruction execution ((computer science) the process of carrying out an instruction by a computer)
S: (n) human process (a process in which human beings are involved)
S: (n) increase, increment, growth (a process of becoming larger or longer or more numerous or more important)
S: (n) industrial process (a systematic series of mechanical or chemical operations that produce or manufacture something)
S: (n) irreversible process (any process that is not reversible)
S: (n) iteration, looping ((computer science) executing the same set of instructions a given number of times or until a specified result isobtained)
S: (n) iteration, loop ((computer science) a single execution of a set of instructions that are to be repeated)
S: (n) natural process, natural action, action, activity (a process existing in or produced by nature (rather than by the intent of human beings))
S: (n) operation, functioning, performance (process or manner of functioning or operating)
S: (n) organic process, biological process (a process occurring in living organisms)
S: (n) photography (the process of producing images of objects on photosensitive surfaces)
S: (n) processing (preparing or putting through a prescribed procedure)
S: (n) reversible process (any process in which a system can be made to pass through the same states in the reverse order when the process isreversed)
S: (n) sensitization, sensitisation ((psychology) the process of becoming highly sensitive to specific events or situations (especially emotiona
events or situations))
S: (n) shaping, defining (any process serving to define the shape of something)
S: (n) variation (the process of varying or being varied)

Hideaki Takeda / National Institute of Informatics

OntologiesllIntroduction of Ontology
n What is Ontology?
n Typology of Ontologies
Top-level Ontologies
n Formal Ontology
u DOLCEu BFO(Basic Formal Ontology)u Sowa’s top-level ontologyn Pragmatic top-level ontology
u SUMOu OpenCyc
n Linguistic top-level ontology
u WordNetu Penman Upper Model/The Generalized Upper Model
Hideaki Takeda / National Institute of Informatics

Linguistic top-level
ontologylPenman Upper Model/The
Generalized Upper Modeln ca. 250 concepts and theirrelationsn Initially as a part of anatural language
generation systemn Things and processes

ＰＲＯＪＥＣＴＩＮＧ－ＣＯＮＦＩＧＵＲＡＴＩＯＮＳＥＱＵＥＮＣＥ

ＥＸＰＡＮＤＩＮＧ－ＣＯＮＦＩＧＵＲＡＴＩＯＮＤＯＩＮＧ＆ＨＡＰＰＥＮＩＮＧＳＡＹＩＮＧ＆ＳＥＮＳＩＮＧ

ＤＩＲＥＣＴＥＤ－ＡＣＴＩＯＮＮＯＮＤＩＲＥＣＴＥＤ－ＡＣＴＩＯＮ
ＩＩＮＴＥＲＮＡＬ－ＰＲＯＣＥＳＳＩＮＧ
ＥＸＴＥＲＮＡＬ－ＰＲＯＣＥＳＳＩＮＧ

ＢＥＩＮＧ＆ＨＡＶＩＮＧ

ＥＸＩＳＴＥＮＣＥＩＮＨＥＲＥＮＴ

ＲＥＬＡＴＩＮＧ

ＣＯＮＴＩＮＧＥＮＴＣＯＮＦＩＧＵＲＡＴＩＯＮＵＭＴＨＩＮＧＤＩＳＴＩＮＣＴＤＹＮＡＭＩＣ－ＱＵＡＬＩＴＹ

ＰＲＯＪＥＣＴＥＤ

ＰＯＬＡＲ－ＱＵＡＬＩＴＹＮＯＮＳＣＡＬＡＢＬＥ－ＱＵＡＬＩＴＹＮＯＮＰＲＯＪＥＣＴＥＤＣＯＮＳＥＱＵＥＮＣＥ－ＯＲＩＥＮＴＥＤ

ＭＡＴＥＲＩＡＬ－ＷＯＲＬＤ－ＱＵＡＬＩＴＹＳＣＡＬＡＢＬＥ－ＱＵＡＬＩＴＹＮＯＮ－ＣＯＮＳＥＱＵＥＮＣＥ－ＯＲＩＥＮＴＥＤＳＩＭＰＬＥ－ＱＵＡＬＩＴＹＳＴＡＴＩＶＥ－ＱＵＡＬＩＴＹＴＡＸＯＮＯＭＩＣ－ＱＵＡＬＩＴＹ

ＬＯＧＩＣＡＬ－ＱＵＡＬＩＴＹＭＯＤＡＬ－ＱＵＡＬＩＴＹ

ＬＯＧＩＣＡＬ－ＵＮＩＱＵＥＮＥＳＳ
ＶＯＬＩＴＩＯＮＡＬＮＯＮＶＯＬＩＴＩＯＮＡＬＣＯＮＤＩＴＩＯＮＡＬＮＯＮＣＯＮＤＩＴＩＯＮＡＬＥＬＥＭＥＮＴＤＥＣＯＭＰＯＳＡＢＬＥ－ＯＢＪＥＣＴＳＩＭＰＬＥ－ＴＨＩＮＧＮＯＮＤＥＣＯＭＰＯＳＡＢＬＥ－ＯＢＪＥＣＴＵＭ－ＳＥＴ
ＯＲＤＥＲＥＤ－ＯＢＪＥＣＴ
ＴＩＭＥ－ＰＯＩＮＴ
ＳＰＡＣＥ－ＰＯＩＮＴ
ＳＵＢＳＴＡＮＣＥＣＯＮＳＣＩＯＵＳ－
ＢＥＩＮＧ
ＰＲＯＣＥＳＳＣＩＲＣＵＭＳＴＡＮＣＥＮＯＮ－ＣＯＮＳＣＩＯＵＳ－ＴＨＩＮＧＰＥＲＳＯＮＳＰＡＴＩＡＬ－ＴＥＭＰＯＲＡＬ
ＡＢＳＴＲＡＣＴＩＯＮ

Hideaki TakedaＰＡＲＴＩＣＩＰＡＮＴ/ National Instituteof Informatics
ＮＡＭＥＤ－ＯＢＪＥＣＴTop-level ontologylThree approachesn Formal approachu Logical formalizationu Fully Abstractu Pros: cleanu Cons: hardly understandable
u e.g., Sowa’s top-level ontology, DOLCE, Basic Formal Ontology (BFO)
n Pragmatic Approachu Use and extension of everyday concepts
u Mostly generalu Pros: understandable and applicable to all the worldu Cons: lack of solid foundation
u e.g. SUMO, OpenCyc, EDR
n Linguistic approachu Use and extension of linguistic concepts
u Partially abstract and partially generalu Pros: understandableu Cons: limitation to the linguistic world
u e.g., Penman Upper Model, WordNet

Hideaki Takeda / National Institute of Informatics

References
l

l

l

DOLCE
n Aldo Gangemi , Nicola Guarino , Claudio Masolo , Alessandro Oltramari , Luc
Schneider, Sweetening Ontologies with DOLCE, Proceedings of the 13th International
Conference on Knowledge Engineering and Knowledge Management. Ontologies andthe Semantic Web, p.166-181, October 01-04, 2002
http://portal.acm.org/citation.cfm?id=650863&dl=GUIDE&coll=GUIDE&CFID=3917
4097&CFTOKEN=41128460
n C. Masolo and S. Borgo and A. Gangemi and N. Guarino and A. Oltramari and L.
Schneider, WonderWeb Deliverable D17. The WonderWeb Library of Foundational
Ontologies and the DOLCE ontology (2002) .
Ontology for Universals
n Nicola Guarino , Christopher A. Welty, A Formal Ontology of Properties, Proceedingsof the 12th European Workshop on Knowledge Acquisition, Modeling andManagement, p.97-112, October 02-06, 2000
http://portal.acm.org/citation.cfm?id=650850&dl=&coll=GUIDE&CFID=39173867&C
FTOKEN=59527108
Ontocleann N. Guarino and C. A. Welty, "An overview of OntoClean," in The Handbook on
Ontologies, (S. Staab and R. Studer, eds.), pp. 151-172, Berlin: Springer-Verlag, 2004.
Hideaki Takeda / National Institute of Informatics

l

BFO
n P. Grenon, B. Smith and L. Goldberg, Biodynamic ontology:
applying BFO in the biomedical domain, Ontologies in medicine,IOS Press, Amsterdam (2004), pp. 20–38.

Hideaki Takeda / National Institute of Informatics