Ontology-Driven Conceptual Modeling · CAISE-02 35 The Backbone Taxonomy Assumption: no entity...
Transcript of Ontology-Driven Conceptual Modeling · CAISE-02 35 The Backbone Taxonomy Assumption: no entity...
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Ontology-Driven Conceptual Modeling
Chris WeltyIBM Watson Research Center
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Acknowledgements
People
Nicola GuarinoCladio MasoloAldo GangemiAlessandro Oltramari
Bill Andersen
OrganizationsVassar College, USA
LADSEB-CNR, PadovaCNR Cognitive Science
Institute, Trento
OntologyWorks, Inc.
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Outline
• Setting the record straight
• Motivation
• Formal foundation
• “Upper Level” distinctions
• Common pitfalls
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What is Ontology?
• A discipline of Philosophy– Meta-physics dates back to Artistotle
• Meta (after) + physica (physical, real)– Ontology dates back to 17th century
• Ontos (that which exists) + logos (knowledge of)• As in TorONTO, ONTario, ON TOp
– The science of what is (in the universe)– “One universe, One ontology”
• Quine, 1969:“To exist is to be the value of a quantified variable”
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What is Ontology?
• Borrowed by AI community– McCarthy (1980) calls for “a list of things that
exist”– Specify all the kinds of things that can be the
values of variables• Evolution of meaning in CS
– Now refers to domain modeling, conceptual modeling, knowledge engineering, etc.
• Note: not a “new name for an old thing”
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What is an Ontology?
• Poor definition:“Specification of a conceptualization” [Gruber, 1993]
• Better:“Description of the kinds of entities there are and
how they are related.”• Good ontologies should provide:
– Meaning– Organization – Taxonomy
– Agreement– Common Understanding– Vocabulary– Connection to the “real world”
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What is an Ontology?
complexity
a catalog
a set of generallogicalaxiomsa glossary
a set of termsa thesaurus
a collection of
taxonomies
a collection of frames
with automated reasoningwithout automated reasoning
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What is an Ontology?
complexity
a catalog
a set of generallogicalaxiomsa glossary
a set of termsa thesaurus
a collection of
taxonomies
a collection of frames
with automated reasoningwithout automated reasoning
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Key Challenges• Must build/design, analyze/evaluate, maintain/extend,
and integrate/reconcile ontologies
• Little guidance on how to do this– In spite of the pursuit of many syntactic standards– Where do we start when building an ontology?– What criteria do we use to evaluate ontologies?– How are ontologies extended?– How are different ontological choices reconciled?
• Ontological Modeling and Analysis– Does your model mean what you intend?– Will it produce the right results?
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Outline
• Setting the record straight
• Motivation
• Formal foundation
• “Upper Level” distinctions
• Common pitfalls
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Motivation
Provide a sound basis for analyzing ontological decisions
“If you can give me a way to shorten the length of the arguments I have with these doctors, you have made a
significant contribution…”-Alan Rector
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Most ontology efforts fail
• Why?– The quality of the ontology dictates its impact– Poor ontology, poor results– Ontologies are built by people
…The average IQ is 100
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Which one is better?
T-Series
ThinkPad
T Series
ThinkPad Model
Thinkpad
model
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Which one is better?
MemoryDisk Drive
Computer
Micro Drive
has-partComputer Part
Computerhas-part Disk Drive ∨ Memory ∨ …
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Contributions• Methodology to help analyze & build consistent ontologies
– Formal foundation of ontological analysis– Meta-properties for analysis– “Upper Level” distinctions
• Standard set of upper-level concepts• Standardizing semantics of ontological relations
• Common ontological modeling pitfalls– Misuse of intended semantics
• Specific recent work focused on clarifying the subsumption(is-a, subclass) relation
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Outline
• Setting the record straight
• Motivation
• Formal foundation
• “Upper Level” distinctions
• Common pitfalls
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Approach
• Draw fundamental notions from Philosophy• Establish a set of useful meta-properties, based
on behavior wrt above notions • Explore the way these meta-properties combine
to form relevant property kinds• Explore the constraints imposed by these
property kinds.
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Basic Philosophical Notions(taken from Formal Ontology)
• Identity– How are instances of a class distinguished from each
other• Unity
– How are all the parts of an instance isolated• Essence
– Can a property change over time• Dependence
– Can an entity exist without some others
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Essence and Rigidity
• Certain entities have essential properties.– Hammers must be hard.– John must be a person.
• Certain properties are essential to all their instances (compare being a person with being hard).
• These properties are rigid - if an entity is ever an instance of a rigid property, it must always be.
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Formal Rigidity• φ is rigid (+R): ∀x ♦♦φ(x) → � φ(x)
– e.g. Person, Apple
• φ is non-rigid (-R): ∃x φ(x) ∧ ¬ � φ(x)– e.g. Red, Male
• φ is anti-rigid (~R): ∀x ♦♦φ(x) → ¬ � φ(x)– e.g. Student, Agent
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Identity and Unity
• Identity: is this my dog?
• Unity: is the collar part of my dog?
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Identity criteria
• Classical formulation:φ(x) ∧ φ(y) → (ρ(x,y) ↔ x = y)
• Generalization:φ(x,t) ∧ φ(y,t’) → (Γ(x,y,t,t’) ↔ x = y)
(synchronic: t = t’ ; diachronic: t≠ t’)
• In most cases, Γ is based on the sameness of certain characteristic features:
Γ(x,y, t ,t’) = ∀z (χ(x,z,t) ∧ χ(y,z,t’))
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A Stronger Notion:Global ICs
• Local IC:φ(x,t) ∧ φ(y,t’) → (Γ(x,y,t,t’) ↔ x = y)
• Global IC (rigid properties only):φ(x,t) → (φ(y,t’) ∧ Γ(x,y,t,t’) ↔ x = y)
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Identity meta-properties• Supplying (global) identity (+O)
– Having some “own” IC that doesn’t hold for a subsuming property
• Carrying (global) identity (+I)– Having an IC (either own or inherited)
• Not carrying (global) identity (-I)
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Unity Criteria
• An object x is a whole under ω iff ω is an equivalence relation that binds together all the parts of x, such that
P(y,x) → (P(z,x) ↔ ω(y,z))but not
ω(y,z) ↔ ∃x(P(y,x) ∧ P(z,x))
• P is the part-of relation• ω can be seen as a generalized indirect connection
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Unity Meta-Properties
• If all instances of a property φ are wholes under the same relation, φ carries unity (+U)
• When at least one instance of φ is not a whole, or when two instances of φ are wholes under different relations, φ does not carry unity (-U)
• When no instance of φ is a whole, φ carries anti-unity (~U)
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Property Dependence
• Does a property holding for x depend on something else besides x? (property dependence) – P(x) → ∃y Q(y)– y should not be a part of x
• Example: Student/Teacher, customer/vendor
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Outline
• Setting the record straight
• Motivation
• Formal foundation
• “Upper Level” distinctions
• Common pitfalls
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“Upper Level” Ontology
• The “media independent” knowledge– Fundamental truths of the universe– Non contextual (aka formal)
• Is there only one?• Upper level ≠ Large• Proven value
– A place to start– Semantic integration
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Upper LevelWhere do I start?
• Particulars– Concrete
• Location, event, object, substance, …– Abstract
• information, story, collection, …• Universals
– Property (Class)– Relation
• Subsumption (subclass), instantiation, constitution, composition (part)
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A formal ontology of properties
Property
Non-sortal-I
Role~R+D
Sortal+I
Formal Role
Attribution -R-D
Category +R
Mixin -D
Type +O
Quasi-type -O
Non-rigid-R
Rigid+R
Material roleAnti-rigid~R Phased sortal -D +L
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Sortals, categories, and other properties• Sortals (horse, triangle, amount of matter, person, student...)
– Carry identity– Usually correspond to nouns– High organizational utility– Main subclasses: types and roles
• Categories (universal, particular, event, substance...)– No identity– Useful generalizations for sortals– Characterized by a set of (only necessary) formal properties– Good organizational utility
• Other non-sortals (red, big, decomposable, eatable, dependent, singular...)– No identity– Correspond to adjectives– Span across different sortals– Limited organizational utility (but high semantic value)
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Formal Ontology of Relations
• Subsumption• Instantiation• Part/Whole• Constitution• Spatial (Cohn)• Temporal (Allen)
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Subsumption• The most pervasive relationship in ontologies
– Influence of taxonomies and OO• AKA: Is-a, a-kind-of, specialization-of, subclass (Brachman, 1983)
– “horse is a mammal”• Capitalizes on general knowledge
– Helps deal with complexity, structure– Reduces requirement to acquire and represent redundant specifics
• What does it mean?
� ∀x φ(x) → ρ(x)
Every instance of the subclass is necessarily an instance of the superclass
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The Backbone TaxonomyAssumption: no entity without identity
Quine, 1969
• Since identity is supplied by types, every entity must instantiate a type
• The taxonomy of types spans the whole domain• Together with categories, types form the backbone
taxonomy, which represents the invariant structure of a domain (rigid properties spanning the whole domain)
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Rigidity Constraint
+R ⊄ ~R
• Why?
� ∀x P(x) → Q(x)
Q~R
P+R
O10
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Identity Conditions along Taxonomies
• Adding ICs:– Polygon: same edges, same angles
• Triangle: two edges, one angle– Equilateral triangle: one edge
• Just inheriting ICs:– Person
• Student
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Identity Disjointness Constraint
Properties with incompatible ICs are disjoint
Besides being used for recognizing sortals, ICs impose constraints on them, making their ontological nature explicit:
Examples:• sets vs. ordered sets• amounts of matter vs. assemblies
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Unity Disjointness Constraint
Properties with incompatible UCs are disjoint+U ⊄ ~U
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Taxonomic Constraints
• +R ⊄ ~R• -I ⊄ +I• -U ⊄ +U• +U ⊄ ~U• -D ⊄ +D
• Incompatible IC’s are disjoint
• Incompatible UC’s are disjoint
• Categories subsume everything
• Roles can’t subsume types
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Outline
• Setting the record straight
• Motivation
• Formal foundation
• “Upper Level” distinctions
• Common pitfalls
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Overloading Subsumption Common modeling pitfalls
• Instantiation• Constitution• Composition• Disjunction• Polysemy
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Instantiation (1)
T21
My ThinkPad (s# xx123)
ThinkPad Model
Ooops…
Question: What ThinkPad models do you sell?Answer should NOT include My ThinkPad -- nor yours.
Does this ontology mean that My ThinkPad is a ThinkPad Model?
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Instantiation (2)
T Series
My ThinkPad (s# xx123)
ThinkPad ModelNotebook Computer
model T 21
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Composition (1)
MemoryDisk Drive
Computer
Question: What Computers do you sell?Answer should NOT include Disk Drives or Memory.
Micro Drive
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Composition (2)
MemoryDisk Drive
Computer
Micro Drive
part-of
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Disjunction (1)
MemoryDisk Drive
Computer
Micro Drive
has-partComputer Part
Flashcard-110Camera-15has-part
Unintended model: flashcard-110 is a computer-part
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Disjunction (2)
Computerhas-part Disk Drive ∨ Memory ∨ …
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Polysemy (1)(Mikrokosmos)
Abstract EntityPhysical Object
Book
Question: How many books do you have on Hemingway?Answer: 5,000
…..
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Polysemy (2)(WordNet)
Abstract EntityPhysical Object
BookSense 1
BookSense 2
….. Biography of Hemingway
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Constitution (1)(WordNet)
Amount of Matter
Physical Object
Entity
ComputerClayMetal
Question: What types of matter will conduct electricity?Answer should NOT include computers.
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Constitution (2)
Amount of Matter Physical Object
Entity
ComputerClayMetal
constituted
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Technical Conclusions• Subsumption is an overloaded relation
– Influence of OO – Force fit of simple taxonomic structures– Leads to misuse of is-a semantics
• Ontological Analysis– A collection of well-defined knowledge structuring relations– Methodology for their consistent application
• Meta-Properties for ontological relations• Provide basis for disciplined ontological analysis
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Applications of Methodology
• Ontologyworks• IBM• Ontoweb• TICCA, WedODE, Galen, …• Strong interest from and participation in
– Semantic web (w3c)– IEEE SUO– Wordnet– Lexical resources
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References• Guarino, Nicola and Chris Welty. 2002. CACM. 45(2):61-65.• Smith, Barry and Chris Welty. 2001. Ontology: Towards a
new synthesis. In Formal Ontology in Information Systems. ACM Press.
• Welty, Chris and Nicola Guarino. 2001. In J. Data and Knowledge Engineering. 39(1):51-74. October, 2001.
• Guarino, Nicola and Chris Welty. 2000. In Proceedings of ER-2000: The 19th International Conference on Conceptual Modeling.
• Guarino, Nicola and Chris Welty. 2000. In Proceedings of EKAW-2000
• Guarino, Nicola and Chris Welty. 2000. In Proceedings of ECAI-2000: The European Conference on Artificial Intelligence.
• Upcoming special issue of AI Magazine on Ontologies.