knowledge base - 北海道大学mhjcc3-ei.eng.hokudai.ac.jp/~yoshioka/kb/kby-3.pdfsparql以外のrdf...
TRANSCRIPT
![Page 1: Knowledge Base - 北海道大学mhjcc3-ei.eng.hokudai.ac.jp/~yoshioka/kb/kby-3.pdfSPARQL以外のRDF データに対するクエリ言語 はないのか? RDFとDublin Coreのできることが知りたい](https://reader034.vdocuments.pub/reader034/viewer/2022051923/60108899e41c9f38235f2dff/html5/thumbnails/1.jpg)
Knowledge Base
―Semantic Web (3)―
Masaharu Yoshioka
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Answer of the last lecture question
Please describe the metadata information as RDF
using Dublin Core Metadata terms
A title of the web site http://www.hokudai.ac.jp/
is 北海道大学 Hokkaido University and described
in Japanese.
<?xml version="1.0" encoding="Shift_JIS" ?> <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/" xml:lang="ja"> <rdf:Description rdf:about="http:// www.hokudai.ac.jp/"> <dc:title>北海道大学 Hokkaido University </dc:title>
<dc:lang>日本語</dc:lang> </rdf:Description> </rdf:RDF>
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Comments from the last report
RDF Information can be changed?
スマートフォンでもRDFは見れるのでしょうか?
SPARQL以外のRDFデータに対するクエリ言語はないのか?
RDFとDublin Coreのできることが知りたい。
実際に自分で書いてみないとイメージがつかめない。
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Comments from the last report
よくわかりませんでした。
I can’t understand what you say in clear
sometimes. Can you show us Japanese slide?
課題がわかりませんでした。
日本語で説明してください。
わかりませんでした。
I didn’t understand how to solve question above.
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Ontology (1)
Oxford English Dictionary
– Philosophy. The science or study of being; that branch
of metaphysics concerned with the nature or essence of
being or existence.
– As a count noun: a theory or conception relating to the
nature of being. Also in extended use.
– Related Terms
• epistemology
– Philosophy. The theory of knowledge and understanding,
esp. with regard to its methods, validity, and scope, and
the distinction between justified belief and opinion; (as a
count noun) a particular theory of knowledge and
understanding.
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Ontology (2)
Ontology in Artificial Intelligence
– Discussion about representation of being in computer
• Two approaches to represent object using qualitative
physics (naïve physics)
– Device ontology
Representation of physical phenomena using device
related to the phenomena
– Process ontology
Representation of physical phenomena using process that
affects the attribute value of the device
• Failure of expert system (production rule)
– Expert system succeeded to extract knowledge of the
domain experts for reasoning.
– However, extracted knowledge is ad-hoc and it was
difficult to reuse such knowledge for other cases.
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Ontology (3)
Definition
– Specification of a conceptualization
by T. Gruber@Stanford
– Systematic description of the concepts for constructing
problem solving system R. Mizoguchi@Osaka U (now
JAIST)
Utilization of Ontology
– Support knowledge reuse
• For reusing the knowledge, it is necessary to understand
the background of the knowledge.
– Communication among agents with different knowledge
• It is necessary to understand the correspondence between
the concept X of agent A and concept X’ of agent B
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Ontology (4)
Similar technology (definition by OED)
– Taxonomy
Classification, esp. in relation to its general laws or
principles; that department of science, or of a particular
science or subject, which consists of or relates to
classification; esp. the systematic classification of
living organisms.
– Thesaurus
A collection of concepts or words arranged according
to sense; also (U.S.) a dictionary of synonyms and
antonyms.
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Logic for Ontology
It is better to have a reasoning framework to
handle concepts.
– Identification of the same concept category
– Concrete-abstract relationship
– Concept category identification based on the instance
attribute
↓
Description Logic
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Background of Description Logic:
Semantic Network
Semantic Network
– Representation of semantic relations between concepts.
• Node represents concepts
• Labeled links represents relations with type
information
Mother Farther
Human
Child
is-a is-a
has-a
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Background of Description Logic: Frame
Frame
– Representing knowledge about concept
– Frame: Attribute Slot + links to another concepts
Hanako
Gender Female
…
Farther Taro
Mother Yoshiko
…
Taro
Gender Male
…
Farther Ichizo
Mother Yoshie
…
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Background of Description Logic:KL-ONE
KL-ONE
– Knowledge representation language based on semantic network and frame
– Representation of concept hierarchy (Abstract-concrete)
• Concept hierarchy are composed by multiple inheritance
– Representing concepts
• Primitive concept : Root concepts defined by the system
• Defined concept : Concepts defined by using inheritance/constraints of pre-defined concept(s)
– Knowledge representation based on the object
• assertion and description
• generic concept and individual concept
– Classification
• When new concepts are given, the system can find out appropriate position of the concept hierarchy.
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Description Logic: DL Language
Knowledge representation language
– The core reasoning problems for DL language are
decidable.
– Varieties of DL languages are proposed for handling
varieties of operators allowed.
– Efficient algorithms are proposed for such languages
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Application of DL Language
Reasoning task used by DL language
– Check satisfiability of the concept and subsumption
relationship among concepts
• Concept satisfiability
Check existence of the concept based on the given
constraints.
• Check subsumption relationship
Calculate subsumption relationship between
different concpets (e.g., Human is subsumed by
Animal)
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Basic element of DL language
Basic element of DL language
– Concept
• A set of individuals that shares particular characteristics.
• Unary predicate of first order logic
– Role
• Attribute of Concept
• Binary predicate of first order logic
– Logical constructors
• ⊓(intersection or conjunction)、 ⊔ (union or disjunction)、¬(negation or compliment)
– Restriction
• (universal restriction)、 (existential restriction)
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Concept Definition using DL Language
Example
– Female ⊓ Human “woman”
– Human ⊓ (has-child.Human) “Parent (human) :Human who has another Human as child”
– Human ⊓ has-child.Male “Human who does not
have any female child:If human has child, child should
be Male”
– Exercise
• What is a concept definition of father who only have
(a) male child(ren).
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ALC Language
Concept Representation in ALC – CN: A set of concept names
– RN: A set of role names
– Let ACN、RRN, C and D are concepts.
Followings are list of concepts.
A|C|C ⊓ D|C ⊔ D| R.C| R.C
– Interpretation I
• Domain I (non-empty set)
• Interpretation function I
– Assign subset of I for all concepts
– Assign subset of I I for all roles
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Constructor of ALC and its Semantics
Constructor Syntax Semantics
Concept C CI I
Role R RI II
Intersection or conjunction C ⊓ D CIDI
Union or disjunction C ⌴ D CIDI
Universal restriction R.C {xI | y.(x,y)RI yCI }
Existential restriction R.C {xI | y.(x,y)RI yCI }
Top T TI = I
Bottom I =
Negation C I-CI
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Knowledge described by DL Language
2 types knowledge – T-Box(Terminological knowledge)
• Intensive knowledge
• Concept definition Parent ≡ Human ⊓ (∃has-child.Human) Mother ≡ Parent ⊓ Female
• Define similar concepts precisely Employee ≡ Worker ⊓ has-boss.(Employee ⌴ Employer) Employee ≡ Worker ⊓ ∃has-boss.(Employee ⌴ Employer)
– A-Box(Assertional knowledge)
• Extensive knowledge
• Knowledge about individual IN Female(YOSHIKO) has-child(YOSHIKO, HANAKO)
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Reasoning by DL Language
Reasoning task of DL Language
– Reasoning using T-Box
• Concept satisfiability
• Concept subusumption relationship analysis
– A-Box
• Consistency
• Check the concept of the given instance
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Tableaux Method
Algorithms to check the concept satisfiability
– Show negation of a given formula cannot be satisfied = a given
formula is true.
• Rewriting formula as NNF (Negational Normal Form)
Negation appears just before the concepts
• Completion rules to rewrite the formula
• Find inconsistency
– {x:A, x:A}
– Tableaux method to judge subsumption relationship(C ⊑ D)
• C and D are replaced with basic concept ACN (C’ and D’)
• Translate C’ ⊓ D’ as NNF and apply tableaux method
– When this formula cannot be satisfied, the subsumption
relationship(C ⊑ D) is justified.
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Completion Rules of Si
Completion Rules
name Condition Translation
⊓-rule x: C1 ⊓ C2 Si
{x: C1, x:C2}Si
Si+1=Si{x: C1, x:C2}
⊔ -rule x: C1 ⊔ C2 Si
{x: C1, x:C2} Si =
Si+1=Si{x: C1} or
Si+1=Si {x:C2}
-rule x: P.C, (x,y):PSi
y: CSi
Si+1=Si{y: C}
-rule x: P.CSi
{z|z: C, (x,z):PSi} =
Si+1=Si{y: C, (x,y):P}
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Check Subsumption Relationship
Mother ⊑ Human
Mother ≡ Parent ⊓ Female
Parent ≡ Human ⊓ has-child.Human
Human ≡ Male ⊔ Female
Mother
Parent ⊓ Female
Human ⊓ has-child.Human ⊓ Female
(Male ⊔ Female) ⊓ has-child.(Male ⊔ Female)) ⊓ Female
(Male ⊔ Female) ⊓ has-child.(Male ⊔ Female)) ⊓ Female
⊓ (Male ⊔ Female)
(Male ⊔ Female) ⊓ has-child.(Male ⊔ Female)) ⊓ Female
⊓ (Male ⊓ Female)
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Check Subsumption Relationship(Continue)
S0={x:(Male ⊔ Female) ⊓ has-child.(Male ⊔ Female)) ⊓
Female ⊓ (Male ⊓ Female)}
S1= S0 {x:(Male ⊔ Female) ⊓ has-child.(Male ⊔ Female)) ⊓ Female,
x: (Male ⊓ Female)}
S2= S1 {x:(Male ⊔ Female) ⊓ has-child.(Male ⊔ Female)),
x: Female}
S3= S2 {x:(Male ⊔ Female), x: has-child.(Male ⊔ Female))}
S4= S3 {x: Male x: Female}
S5’ = S4 {x:Female} S5= S4 {x: Male}
⊓-rule
⊓-rule
⊓-rule
⊓-rule
⊔ -rule
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ALC Language Family(1)
Cardinality Restrictions N
– At most n relations (R) nR
• 3has-child ⊓ Male Male who has at most 3 children
– At least n relations nR
• 3has-member ⊓ Team Team who has at most 3 members
Qualified Cardinality Restrictions Q
– At most n indviduals of C for relation R nR.C
• 3has-child.Male ⊓ Male Male who has at least 3 children
• 3has-member.Female ⊓ Team Team who has at least 3
members
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ALC Language Family(2)
Role operator R
– Satisfy two Roles at the same time R1 ⊓ R2
• (has-parent ⊓ has-Teacher).Male whose farther is also a teacher
Inverse Role I
– Inverse of relation R R-
• has-parent - ≡has-child
• has-parent -.Female ⊓ Human peopole who has female child
One-of O
– Defined by a set of instances {a1 ,a2 , ,an } • {Spring, Summer, Autumn, Winter}
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DL Processor
CLASSIC (1991)
– Old DL processor developed by Bell laboratory, AT&T
– Not available now
Standards for Semantic-Web
– OWL(Web Ontology Language)
– DAML (DARPA Agent Markup Language) +OIL (Ontology Interface Layer)
Tools for describing ontology
– Example:RACER https://www.ifis.uni-luebeck.de/index.php?id=385 ALCQHIR+ (D-)
• Support OWL
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OWL https://www.w3.org/OWL/
OWL: Web Ontology Language
– For precise definition of concepts using with RDF and
RDF Schema
– Concept definition by Description Logic
• Class hierarchy
• Attribute definition
• Intensive concept definition
• Extensive concept definition
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History of OWL
DAML+OIL
– DARPA Agent Markup Language Ontology Interface Layer
– Ontology for communication among different agents
OWL
– Extension of DAML+ OIL for usage at Semantic Web application
– Three variations that are take into account the balance between the
implementation cost and expressiveness
• OWL-Lite: The simplest language with lower expressiveness.
• OWL-DL:Add more expressiveness by using the Description
Logic framework.
• OWL-Full:No implementation restriction to represent
concepts appropriately.
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Class definition
Extension of class definition in RDF Schema
– owl:class Subclass of (rdfs:subClassOf)
rdfs:class and、relationships defined in rdfs can be used
for describing resource using OWL.
– Class and instance(Individual)
owl:Thing is an instance(Individual) and associated
with class definition by using rdf:type
<owl:Thing rdf:about="#CentralCoastRegion">
<rdf:type rdf:resource="#Region"/>
</owl:Thing>
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Property Definition
Property of class
replacement for rdf:Property
– owl:ObjectProperty Property of the object
– owl:DatatypeProperty
– Vocabulary used from RDF Schema
rdfs:subPropertyOf, rdfs:domain, rdfs:range
Type of property
– owl:transitiveProperty p(x,y) and p(y,z) → p(x,z)
– owl:symmetricProperty p(x,y) → p(y,x)
– owl:functionalProperty p(x,y) and p(x,z)→ y=z
– owl:inverseOf p1(x,y) → p2(y,x)
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Constraints for the property
owl:restriction
– allValuesFrom, someValuesFrom
Values of the properties should be selected from the list
– owl:minCardinality
minimum cardinality: If it is 0 or 1, it represents
property is required or not(OWL-Lite)、can use integer
(OWL-DL)
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Class Definition based on Property
Intensive definition of the class
– Class characterized by the property value.
• owl:restriction
<owl:Class rdf:ID="Wine">
<rdfs:subClassOf rdf:resource="&food;PotableLiquid"/>
<rdfs:subClassOf>
<owl:Restriction>
<owl:onProperty rdf:resource="#madeFromGrape"/>
<owl:minCardinality rdf:datatype="&xsd;nonNegativeInteger">
1</owl:minCardinality>
</owl:Restriction>
</rdfs:subClassOf>
...
</owl:Class>
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Relationship among Other Ontologies
Description about the relationship with other ontology
– owl:equivalentClass Same class of the class defined in other ontology.
– owl:equivalentProperty Same property of the property defined in other ontology.
– owl:sameAs Same individual defined in other ontology
– owl:differentFrom, owl:allDifferent Define the individuals are different from individual defined in other ontology explicitly
Reasoning about the correspondence between the concepts defined in other ontology
<owl:Class rdf:ID="Wine">
<owl:equivalentClass rdf:resource="&vin;Wine"/>
</owl:Class>
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Advanced Class definition (OWL-DL)
Set operator based definition
– intersectionOf, unionOf, complementOf
Enumerated definition
– one of : Define class by using set of individuals
Two class descriptions have no individuals in
common
– disjointWith
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OWL Full
Can handle class as instance
– For example
• Class: Car and Japanese Car
• Instance Crown, Fit, Note,…
• However,
– There are cases that instance of Fit (Fit owned by a driver)
is necessary for the knowledge representation.
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Integration of Multiple Ontologies
Ontology Matching
– Calculate similarity based on the description and/or
instance
– Ontology matching browser based on Web2.0 approach
![Page 38: Knowledge Base - 北海道大学mhjcc3-ei.eng.hokudai.ac.jp/~yoshioka/kb/kby-3.pdfSPARQL以外のRDF データに対するクエリ言語 はないのか? RDFとDublin Coreのできることが知りたい](https://reader034.vdocuments.pub/reader034/viewer/2022051923/60108899e41c9f38235f2dff/html5/thumbnails/38.jpg)
Summary
Ontology
– Specification of conceptualization
– Represent relationship among different concepts
Semantic Web:Understanding the semantic of the
Web
– Standardization of structured data representation
⇒XML, RDF, ...
– Standardization of concept
⇒Ontology