meghyn slides-hse-2014
TRANSCRIPT
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Ontologies in Computer Science:The Description Logic Approach
Meghyn Bienvenu
Laboratoire de Recherche en Informatique,CNRS & Universite Paris-Sud, France
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What is an ontology?
Depends on who you ask...
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What is an ontology?
Depends on who you ask...
In philosophy:the study of the nature of being, becoming, existence, or reality, as well as thebasic categories of being and their relations (source: wikipedia)
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What is an ontology?
Depends on who you ask...
In philosophy:the study of the nature of being, becoming, existence, or reality, as well as thebasic categories of being and their relations (source: wikipedia)
In computer science:the formal specification of the knowledge of a particular domain,thereby making it amenable to machine processing
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What is an ontology?
Depends on who you ask...
In philosophy:the study of the nature of being, becoming, existence, or reality, as well as thebasic categories of being and their relations (source: wikipedia)
In computer science:the formal specification of the knowledge of a particular domain,thereby making it amenable to machine processing
Such a specification consists of:
• terminology (or vocabulary) of the domain
• semantic relationships between terms
– relations of specificity or generality, equivalence, disjointness, ...
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What are ontologies good for?
To standardize the terminology of an application domain
• meaning of terms is constrained, so less misunderstandings
• by adopting a common vocabulary, easy to share information
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What are ontologies good for?
To standardize the terminology of an application domain
• meaning of terms is constrained, so less misunderstandings
• by adopting a common vocabulary, easy to share information
To present an intuitive and unified view of data sources
• ontology can be used to enrich the data vocabulary, making it easier forusers to formulate their queries
• especially useful when integrating multiple data sources
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What are ontologies good for?
To standardize the terminology of an application domain
• meaning of terms is constrained, so less misunderstandings
• by adopting a common vocabulary, easy to share information
To present an intuitive and unified view of data sources
• ontology can be used to enrich the data vocabulary, making it easier forusers to formulate their queries
• especially useful when integrating multiple data sources
To support automated reasoning
• uncover implicit connections between terms, or errors in modelling
• exploit knowledge in the ontology during query answering, to get back amore complete set of answers to queries
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Ontologies in Medecine
Comprehensive medical ontologies: SNOMED CT (∼ 400,000 terms!), GALEN
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Ontologies in Medecine
Comprehensive medical ontologies: SNOMED CT (∼ 400,000 terms!), GALEN
Specialized ontologies: FMA (anatomy), NCI (cancer), ...
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Ontologies in Medecine
Comprehensive medical ontologies: SNOMED CT (∼ 400,000 terms!), GALEN
Specialized ontologies: FMA (anatomy), NCI (cancer), ...
• querying & exchanging medical records (e.g. find patients eligible for medical trials)
– myocardial infarction vs. MI vs. heart attack vs. 410.0
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Ontologies in Medecine
Comprehensive medical ontologies: SNOMED CT (∼ 400,000 terms!), GALEN
Specialized ontologies: FMA (anatomy), NCI (cancer), ...
• querying & exchanging medical records (e.g. find patients eligible for medical trials)
– myocardial infarction vs. MI vs. heart attack vs. 410.0
• supports tools for annotating and visualizing patient data (scans, x-rays)
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Ontologies in Biology
Hundreds of ontologies at BioPortal (http://bioportal.bioontology.org/):Gene Ontology (GO), Cell Ontology, Pathway Ontology, Plant Anatomy, ...
• help scientists to share, query, compare, and visualize experimental data
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Description Logics in a Nutshell
Description logics (DLs) are a family of knowledge representation languages.
• originated from work on semantic networks, frame languages
• nowadays, popular means for specifying ontologies
• basis of the web ontology language OWL (W3C standard)
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Description Logics in a Nutshell
Description logics (DLs) are a family of knowledge representation languages.
• originated from work on semantic networks, frame languages
• nowadays, popular means for specifying ontologies
• basis of the web ontology language OWL (W3C standard)
Formally: fragments of first-order logic.
• inherit well-defined semantics
• succinct, variable-free syntax
• related to modal logics, guarded fragment, ...
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Description Logics in a Nutshell
Description logics (DLs) are a family of knowledge representation languages.
• originated from work on semantic networks, frame languages
• nowadays, popular means for specifying ontologies
• basis of the web ontology language OWL (W3C standard)
Formally: fragments of first-order logic.
• inherit well-defined semantics
• succinct, variable-free syntax
• related to modal logics, guarded fragment, ...
Computational properties well-understood (decidability, complexity)
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Description Logics in a Nutshell
Description logics (DLs) are a family of knowledge representation languages.
• originated from work on semantic networks, frame languages
• nowadays, popular means for specifying ontologies
• basis of the web ontology language OWL (W3C standard)
Formally: fragments of first-order logic.
• inherit well-defined semantics
• succinct, variable-free syntax
• related to modal logics, guarded fragment, ...
Computational properties well-understood (decidability, complexity)
Many implemented reasoners and tools available for use
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Description Logic Basics
Building blocks of DLs:
• atomic concepts (unary predicates, classes)
Animal, Dog, Cow, Giraffe, Plant, Herbivore, Carnivore, Domesticated...
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Description Logic Basics
Building blocks of DLs:
• atomic concepts (unary predicates, classes)
Animal, Dog, Cow, Giraffe, Plant, Herbivore, Carnivore, Domesticated...
• atomic roles (binary predicates, properties)
eats, parentOf, hasPart, hasHabitat, ...
• individuals (constants)
fluffy, giraffe0354, specimen32091, ...
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Description Logic Basics
Can build complex concepts and roles using constructors:
• conjunction (u), disjunction (t), negation (¬)
Dog t Cat Animal u ¬Carnivore Plant u Carnivore
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Description Logic Basics
Can build complex concepts and roles using constructors:
• conjunction (u), disjunction (t), negation (¬)
Dog t Cat Animal u ¬Carnivore Plant u Carnivore
• restricted forms of existential and universal quantification (∃, ∀)
∃parentOf.> ∃eats.Animal Animal u ∀eats.Plant
( > acts as a “wildcard”, denotes set of all things)
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Description Logic Basics
Can build complex concepts and roles using constructors:
• conjunction (u), disjunction (t), negation (¬)
Dog t Cat Animal u ¬Carnivore Plant u Carnivore
• restricted forms of existential and universal quantification (∃, ∀)
∃parentOf.> ∃eats.Animal Animal u ∀eats.Plant
( > acts as a “wildcard”, denotes set of all things)
• inverse (−) and composition (◦) of roles
parentOf− parentOf ◦ parentOf
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Description Logic Basics
Can build complex concepts and roles using constructors:
• conjunction (u), disjunction (t), negation (¬)
Dog t Cat Animal u ¬Carnivore Plant u Carnivore
• restricted forms of existential and universal quantification (∃, ∀)
∃parentOf.> ∃eats.Animal Animal u ∀eats.Plant
( > acts as a “wildcard”, denotes set of all things)
• inverse (−) and composition (◦) of roles
parentOf− parentOf ◦ parentOf
NB: available constructors depends on the particular DL
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Description Logic Knowledge Bases
DL knowledge bases (KBs) consist of two parts.
TBox (‘T’ for “terminology”)Contains general knowledge about the domain of interest (axioms)
• Plants and animals are disjoint classes of living things
• Giraffes are animals that only eat leaves
• Herbivores are animals that eat only plants
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Description Logic Knowledge Bases
DL knowledge bases (KBs) consist of two parts.
TBox (‘T’ for “terminology”)Contains general knowledge about the domain of interest (axioms)
• Plants and animals are disjoint classes of living things
• Giraffes are animals that only eat leaves
• Herbivores are animals that eat only plants
ABox (‘A’ for “assertion”)Contains facts about specific individuals (assertions)
• Fluffy is a dog. Fluffy is the parent of Sparky.
• Specimen0305 is a Venus fly trap.
NB: open world assumption (unlike databases)
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Examples of TBox Axioms
Plants and animals are disjoint classes of living things
Plant v LivingThing Animal v LivingThing Plant v ¬Animal
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Examples of TBox Axioms
Plants and animals are disjoint classes of living things
Plant v LivingThing Animal v LivingThing Plant v ¬Animal
Giraffes are animals that only eat leaves
Giraffe v Animal u ∀eats.Leaf
FOL-translation: ∀x (Giraffe(x)→ (∀y eats(x, y)→ Leaf(y)))
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Examples of TBox Axioms
Plants and animals are disjoint classes of living things
Plant v LivingThing Animal v LivingThing Plant v ¬Animal
Giraffes are animals that only eat leaves
Giraffe v Animal u ∀eats.Leaf
FOL-translation: ∀x (Giraffe(x)→ (∀y eats(x, y)→ Leaf(y)))
Herbivores are animals that eat only plants
Herbivore v Animal u ∀eats.Plant Animal u ∀eats.Plant v Herbivore
Abbreviation: Herbivore ≡ Animal u ∀eats.Plant
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A Few More Examples
Defining grandparents
Grandparent ≡ ∃parentOf.∃parentOf.>
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A Few More Examples
Defining grandparents
Grandparent ≡ ∃parentOf.∃parentOf.>
Defining the childOf and grandchildOf relations
childOf ≡ parentOf− grandchildOf ≡ childOf ◦ childOf
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A Few More Examples
Defining grandparents
Grandparent ≡ ∃parentOf.∃parentOf.>
Defining the childOf and grandchildOf relations
childOf ≡ parentOf− grandchildOf ≡ childOf ◦ childOf
Asserting transitivity of binary relations
partOf ◦ partOf v partOf
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Description Logic Semantics (What does it all mean?)
Interpretation I (“possible world”)
• (possibly infinite) domain of objects ∆I
• atomic concept A ; set of objects AI ⊆ ∆I (which objects are A’s?)
• atomic role r ; pair of objects rI (which objects are in relation r?)
• individual a ; object aI (which object is a?)
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Description Logic Semantics (What does it all mean?)
Interpretation I (“possible world”)
• (possibly infinite) domain of objects ∆I
• atomic concept A ; set of objects AI ⊆ ∆I (which objects are A’s?)
• atomic role r ; pair of objects rI (which objects are in relation r?)
• individual a ; object aI (which object is a?)
• complex concepts / roles ; sets / pairs of objectse.g. (C uD)I = CI ∩DI (¬C)I = ∆I \ CI
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Description Logic Semantics (What does it all mean?)
Interpretation I (“possible world”)
• (possibly infinite) domain of objects ∆I
• atomic concept A ; set of objects AI ⊆ ∆I (which objects are A’s?)
• atomic role r ; pair of objects rI (which objects are in relation r?)
• individual a ; object aI (which object is a?)
• complex concepts / roles ; sets / pairs of objectse.g. (C uD)I = CI ∩DI (¬C)I = ∆I \ CI
Satisfaction in an interpretation
• I satisfies C v D ⇔ CI ⊆ DI
• I satisfies C(a) ⇔ aI ∈ CI
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Description Logic Semantics (What does it all mean?)
Interpretation I (“possible world”)
• (possibly infinite) domain of objects ∆I
• atomic concept A ; set of objects AI ⊆ ∆I (which objects are A’s?)
• atomic role r ; pair of objects rI (which objects are in relation r?)
• individual a ; object aI (which object is a?)
• complex concepts / roles ; sets / pairs of objectse.g. (C uD)I = CI ∩DI (¬C)I = ∆I \ CI
Satisfaction in an interpretation
• I satisfies C v D ⇔ CI ⊆ DI
• I satisfies C(a) ⇔ aI ∈ CI
Model of a KB K = interpretation that satisfies all axioms & assertions in K
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Example: Interpretations
∆I
fluffyI
AnimalI
HerbivoreI
PlantI
Which axioms and assertions are satisfied in I?
Animal v ¬Plant Herbivore v Animal Herbivore v ∀eats.Plant
Animal(fluffy) Herbivore(fluffy)
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Reasoning Tasks
SatisfiabilityDoes the KB have a model?
• want to test whether the KB is internally coherent (e.g. no contradictions)
• essential tool for debugging ontologies
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Example: Satisfiability
Suppose our KB contains the following statements:
Plant v ¬Animal
VenusFlyTrap v CarnivorousPlant
VenusFlyTrap(speciment0305)
CarnivorousPlant ≡ Plant u Carnivore
Carnivore ≡ Animal u ∃eats.Animal
Question: Is this KB satisfiable?
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Example: Satisfiability
Suppose our KB contains the following statements:
Plant v ¬Animal
VenusFlyTrap v CarnivorousPlant
VenusFlyTrap(speciment0305)
CarnivorousPlant ≡ Plant u Carnivore
Carnivore ≡ Animal u ∃eats.Animal
Question: Is this KB satisfiable?
No!
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Example: Satisfiability
Suppose our KB contains the following statements:
Plant v ¬Animal
VenusFlyTrap v CarnivorousPlant
VenusFlyTrap(speciment0305)
CarnivorousPlant ≡ Plant u Carnivore
Carnivore ≡ Animal u ∃eats.Animal
Question: Is this KB satisfiable?
No!
Can infer Plant(speciment0305) and Animal(speciment0305)
This contradicts the axiom Plant v ¬Animal
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Reasoning Tasks
SatisfiabilityDoes the KB have a model?
SubsumptionIs it the case that concept C is a subset of D in all models?
• want to understand the relationship between different concepts
• classification: organize atomic concepts into hierarchy
• useful both for visualizing, exploring, and debugging ontologies
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Example: Subsumption
Giraffe v Animal u ∀eats.Leaf
Tree v Plant
Leaf v ∃partOf.Tree
Herbivore ≡ Animal u (∀eats.¬Animal) u (∀eats.¬(∃partOf.Animal))
Animal t ∃partOf.Animal v ¬(Plant t ∃partOf.Plant)
Claim: the above implies Giraffe v Herbivore
Why?
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Reasoning Tasks
SatisfiabilityIs there an interpretation that satisfies all axioms and assertions in the KB?
SubsumptionIs it the case that concept C is always interpreted as a subset of D?
Instance CheckingFind all individuals a that are always interpreted as belonging to C.
• want to infer to which classes the individuals belong
• simple way of querying the ABox (“data”)
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Reasoning Tasks
SatisfiabilityIs there an interpretation that satisfies all axioms and assertions in the KB?
SubsumptionIs it the case that concept C is always interpreted as a subset of D?
Instance CheckingFind all individuals a that are always interpreted as belonging to C.
• want to infer to which classes the individuals belong
• simple way of querying the ABox (“data”)
Conjunctive Query AnsweringFind all tuple of individuals ~a that always satisfy q.
• want to perform database-style querying over DL KBsconjunctive queries = select-project-join queries in SQL
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Example: Conjunctive Queries
Suppose our KB contains the following statements:
MountainGorilla v Gorilla
Gorilla v Primate
MountainGorilla v CriticallyEndangered
keptAt(molly, berlin zoo), MountainGorilla(molly), Zoo(berlin zoo)
Example conjunctive query
q(x, y) = Primate(x) ∧ CriticallyEndangered(x) ∧ keptAt(x, y) ∧ Zoo(y)
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Complexity vs. Expressivity
Extensive investigations of computational complexity of reasoning
• wide range of DLs (from very simple to highly expressive)
• many reasoning tasks, different complexity measures
Nowadays, complexity landscape is well-understood
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Complexity vs. Expressivity
Extensive investigations of computational complexity of reasoning
• wide range of DLs (from very simple to highly expressive)
• many reasoning tasks, different complexity measures
Nowadays, complexity landscape is well-understood
• for most DLs, reasoning is provably intractable (EXPTIME-hard or worse)
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Complexity vs. Expressivity
Extensive investigations of computational complexity of reasoning
• wide range of DLs (from very simple to highly expressive)
• many reasoning tasks, different complexity measures
Nowadays, complexity landscape is well-understood
• for most DLs, reasoning is provably intractable (EXPTIME-hard or worse)
• useful tractable DLs have been identified
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Example: Complexity vs. Expressivity
“Standard” description logic ALC
C := > | A | ¬C | C u C | C t C | ∃r.C | ∀r.C
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Example: Complexity vs. Expressivity
“Standard” description logic ALC
C := > | A | ¬C | C u C | C t C | ∃r.C | ∀r.C EXPTIME-complete
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Example: Complexity vs. Expressivity
“Standard” description logic ALC
C := > | A | ¬C | C u C | C t C | ∃r.C | ∀r.C EXPTIME-complete
Same holds for ALCI (= ALC + inverse roles r−)
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Example: Complexity vs. Expressivity
“Standard” description logic ALC
C := > | A | ¬C | C u C | C t C | ∃r.C | ∀r.C EXPTIME-complete
Same holds for ALCI (= ALC + inverse roles r−)
“Lightweight” description logic EL
C := > | A | C u C | ∃r.C
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Example: Complexity vs. Expressivity
“Standard” description logic ALC
C := > | A | ¬C | C u C | C t C | ∃r.C | ∀r.C EXPTIME-complete
Same holds for ALCI (= ALC + inverse roles r−)
“Lightweight” description logic EL
C := > | A | C u C | ∃r.C PTIME-complete
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Example: Complexity vs. Expressivity
“Standard” description logic ALC
C := > | A | ¬C | C u C | C t C | ∃r.C | ∀r.C EXPTIME-complete
Same holds for ALCI (= ALC + inverse roles r−)
“Lightweight” description logic EL
C := > | A | C u C | ∃r.C PTIME-complete
What about ELI (= EL + inverse roles r−) ?
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Example: Complexity vs. Expressivity
“Standard” description logic ALC
C := > | A | ¬C | C u C | C t C | ∃r.C | ∀r.C EXPTIME-complete
Same holds for ALCI (= ALC + inverse roles r−)
“Lightweight” description logic EL
C := > | A | C u C | ∃r.C PTIME-complete
What about ELI (= EL + inverse roles r−) ? EXPTIME-complete
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Main Reasoning Paradigms
Tableau method (used for expressive DLs)
• Idea: to show that T |= C v D, try to build a countermodel, i.e.a model of T that contains an individual in C u ¬D
• Shows good performance on reasonably-sized ontologies
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Main Reasoning Paradigms
Tableau method (used for expressive DLs)
• Idea: to show that T |= C v D, try to build a countermodel, i.e.a model of T that contains an individual in C u ¬D
• Shows good performance on reasonably-sized ontologies
Forward chaining (aka saturation, consequence-based reasoning)
• Works for Horn DLs (e.g. EL), with no disjunction
• Apply the axioms as rules to generate the consequence of the KB,check if produce desired subsumption / assertion
• Can classify SNOMED in a few seconds!
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Main Reasoning Paradigms
Tableau method (used for expressive DLs)
• Idea: to show that T |= C v D, try to build a countermodel, i.e.a model of T that contains an individual in C u ¬D
• Shows good performance on reasonably-sized ontologies
Forward chaining (aka saturation, consequence-based reasoning)
• Works for Horn DLs (e.g. EL), with no disjunction
• Apply the axioms as rules to generate the consequence of the KB,check if produce desired subsumption / assertion
• Can classify SNOMED in a few seconds!
Backward chaining (aka query rewriting)
• Used for conjunctive query answering in DL-Lite family of lightweight DLs
• Allows us to reduce query answering in DLs to database querying
Primate(x) ; Gorilla(x) ∨ Chimpanzee(x) ∨ . . . ∨ Lemur(x)
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Current Topics in DL Research
Improving scalability of DL reasoners
• need to be able to handle very large KBs (esp. ABoxes ∼ datasets)
• much work focuses on conjunctive queries, lightweight DLs (EL, DL-Lite)
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Current Topics in DL Research
Improving scalability of DL reasoners
• need to be able to handle very large KBs (esp. ABoxes ∼ datasets)
• much work focuses on conjunctive queries, lightweight DLs (EL, DL-Lite)
Reasoning support for building and maintaining ontologies
• explanation (locating source of inconsistencies / undesired subsumptions)
• modularity (how to extract relevant subset of an ontology)
• smart versioning tools (syntactic vs. semantic difference)
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Current Topics in DL Research
Improving scalability of DL reasoners
• need to be able to handle very large KBs (esp. ABoxes ∼ datasets)
• much work focuses on conjunctive queries, lightweight DLs (EL, DL-Lite)
Reasoning support for building and maintaining ontologies
• explanation (locating source of inconsistencies / undesired subsumptions)
• modularity (how to extract relevant subset of an ontology)
• smart versioning tools (syntactic vs. semantic difference)
Capturing and reasoning with “imperfect” knowledge
• vagueness (what does it mean to be “tall”?)
• contextual information (“tall for an elephant” or “tall for a building”?)
• defeasible reasoning (“birds usually fly, but penguins don’t”)
• inconsistency-tolerance (draw sensible inferences despite contradictions)
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Thanks for your attention!
Questions?