towards collaborative environments for ontology construction and sharing
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CTS 2006 Las Vegas, USA. May 15, 2006 1
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
Towards Collaborative Environments for Ontology Construction and Sharing
Jie Bao, Doina Caragea and Vasant Honavar
Artificial Intelligence Research LaboratoryComputer Science Department
Iowa State University, Ames, IA USA 50011Email: {baojie, dcaragea, honavar}@cs.iastate.edu
May 15, 2006
CTS 2006 Las Vegas, USA. May 15, 2006 2
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
Outline
• Motivation
• Package-based Description Logics: Language Features
• Package-based Description Logics : Semantics
• Collaborative Ontology Building Tools
CTS 2006 Las Vegas, USA. May 15, 2006 3
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
Challenges in Ontology Building
• Collaboration Challenges– Integration of local points of view– Avoiding inconsistencies and unintended coupling– Selective knowledge hiding– Partial ontology reuse
• Scaleability Challenges– Editing– Storage– Reasoning
CTS 2006 Las Vegas, USA. May 15, 2006 4
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
Local vs Global Semantics
• Ontologies represent local views of its producers – Biologist: dog species only eats animal
Ontology: Dog is Carnivore and all Carnivore only eats Animal
– Pet owner: pet dog sometimes eats DogFood, which is not animalOntology: PetDog is Dog and some PetDog eats DogFood; DogFood is CannedFood and not Animal
• Global semantics could lead to conflicts
• Localizing knowledge is helpful to reduce such risks
CTS 2006 Las Vegas, USA. May 15, 2006 5
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
Partial vs All-or-Nothing Reuse
General Pet
Poultry Livestock
Animal Ontology(Centralized)
MyPet
General
Pet
Poultry
Livestock
MyPet
Animal Ontology(Package-extended)
Semantic importing
Semantics incorporated in MyPet ontology
Semantics not presented in MyPet ontologyLegend:
• Lack of modularity: all or nothing – Eg: how to import part
of the animal ontology?
• Modular ontologies : more flexible partial reuse– Less communication – Less memory– Less parsing time.– Less unwanted junk!
CTS 2006 Las Vegas, USA. May 15, 2006 6
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
Organizational vs Semantic Structure
Animal
is a part of
• Organizational structure: how to arrange terms for better usage and understanding– Eg: Computer Science Dictionary and
Biology Dictionary
• Semantic structure: how to relate meanings of terms– Eg: ‘Mouse’ is a kind of ‘Animal’ or
‘Mouse’ is part of ‘Computer’
CTS 2006 Las Vegas, USA. May 15, 2006 7
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
Knowledge Hiding vs Sharing
• Ontology reflects shared knowledge in general
Locally visible:Has date
Globally visible:Has activity
Bob’ schedule ontology
• However, the provider may also wish to hide part of it. – Privacy, Copyright, Security
• Partial hiding helps for safer ontology organization– Reduce unexpected coupling
– Separate “details” and “interface”
CTS 2006 Las Vegas, USA. May 15, 2006 8
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
Ontology Languages Today
• Description Logics(DL), OWL, OBO (life science ontologies)
• However, the state of art in ontology languages is reminiscent of the early programming languages
– Uncontrolled use of global terms – Unwanted and uncontrolled interactions between fragments
– Difficult to reuse: all or nothing
CTS 2006 Las Vegas, USA. May 15, 2006 9
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
Ontology Languages Needed
• Modularity– Has localized terminology and semantics– Allows partial ontology reuse– Utilizes organizational and semantic structure – Enables collaborative and scaleable tools
• Knowledge Hiding– Builds safer ontologies– Reduces unwanted interactions– Hides details (encapsulate semantics)
CTS 2006 Las Vegas, USA. May 15, 2006 10
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
Outline
• Motivation
• Package-based Description Logics: Language Features
• Package-based Description Logics: Semantics
• Collaborative Ontology Building Tools
CTS 2006 Las Vegas, USA. May 15, 2006 11
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
P-DL
P3
protected
1. Whole ontology consists of a set of packages
2. Packages are organized in hierarchies
3. Terms and axioms are defined in packages with scope limitations
P1
P2
public
private
P1
P2
public
private
CTS 2006 Las Vegas, USA. May 15, 2006 12
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
Package• A package is an ontology
module with clearly defined access interface;
• Each package is defined with certain ontology language and – Import: terms from other
packages– Interface: terms visible to other
packages
• Each term has a home package
1. Whole ontology consists of a set of packages
General Pet
Poultry Livestock
Animal ontology
Hound, PointerPet
DogGeneral
P3
protected
P1
P2
public
private
P1
P2
public
private
CTS 2006 Las Vegas, USA. May 15, 2006 13
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
Nested Package • A nested package is part of
another package– Super package, sub package– Form a package hierarchy
• Could be used to represent the organizational structure– Arrange knowledge– Enforce hierarchical
management of knowledge
2. Packages are organized in hierarchies
General
Pet
Dog
Animal ontology
P3
protected
P1
P2
public
private
P1
P2
public
private
CTS 2006 Las Vegas, USA. May 15, 2006 14
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
Scope Limitation Modifier • Defines the visible scope of a term or
axiom• SLM of an ontology term or axiom t
– is a boolean function V(t,r), where r is a package
– Package r could access t iff V(t,r) = True.
• Example SLMs– Public (t,r): t is accessible from anywhere– Private (t,r): t is only available in the home
package– Protected(t,r): t is accessible from the
home package and its recursive sub packages.
3. Terms has scope limitation
P3
protected
P1
P2
public
private
P1
P2
public
private
P3
protected
P1
P2
public
private
P1
P2
public
private
CTS 2006 Las Vegas, USA. May 15, 2006 15
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
SLM: example(TBC)A schedule ontology
Hidden: details of the activity
Visible: there is an activity
Hidden semantics may still be used in reasoning
CTS 2006 Las Vegas, USA. May 15, 2006 16
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
Outline
• Motivation
• Package-based Description Logics: Language Features
• Package-based Description Logics : Semantics
• Collaborative Ontology Building Tools
CTS 2006 Las Vegas, USA. May 15, 2006 17
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
Local Interpretation
Carnivore IP
AnimalIP
ΔIP
eatsIPgoofyIP
Ontology: Carnivore AnimalInterpretation: In any world that conforms to the
ontology, for any instance x of Carnivore, x is also an instance of Animal.
CTS 2006 Las Vegas, USA. May 15, 2006 18
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
Local and Global Interpretations
AnimalI
CarnivoreI
DogI
I
PetDogI
goofyI
PetI
eatsI
g
g
g
g
g
g
g
fooIg
DogFoodI g
AnimalI
CarnivoreI
DogI
goofyI fooI
DogI
PetIPetDogI
plutoI
eatsI
1
1
1
1
2
2
2
2
2
2
DogFoodI 2
AnimalI2
=
CTS 2006 Las Vegas, USA. May 15, 2006 19
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
Distributed Interpretation
• Global interpretations may not exist for all packages
• Distributed interpretations may still exist for selected sets of packages.
A BC D
1B CC P
2B,C
B C
3
B,C=
x x’
BI2 = CI2 =PI2 AI1 = BI1,CI1 =DI1
=x x’
BI3
y
AI1 = BI1
CI1= DI1
y’
CI3
P1,P3
P1,P2
CTS 2006 Las Vegas, USA. May 15, 2006 20
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
Outline
• Motivation
• Package-based Description Logics : Language Features
• Package-based Description Logics : Semantics
• Collaborative Ontology Building
CTS 2006 Las Vegas, USA. May 15, 2006 21
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
Collaborative Ontology Building
Ontology modularity facilitates collaborative building
• Each package can be independently developed• Different curators can concurrently edit the
ontology on different packages• Ontology can be only partially loaded• Unwanted interactions are minimized by limiting
term and axiom visibility• Module access privileges can be controlled by
the package hierarchy
CTS 2006 Las Vegas, USA. May 15, 2006 22
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
The INDUS DAG EditorThe COB Editor
CTS 2006 Las Vegas, USA. May 15, 2006 23
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
Summary
• Collaborative ontology building calls for modular ontology representation.
• Package-based description logics (P-DL) offers an ontology language for modularity and selective knowledge sharing.
• Efficient collaborative ontology building tools can be realized with P-DL.
Ongoing Work• Reasoning algorithm• Extension to OWL
CTS 2006 Las Vegas, USA. May 15, 2006 24
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
Backup
CTS 2006 Las Vegas, USA. May 15, 2006 25
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
Ontology Languages Today (2)
• Distributed Description Logics (DDL)– Allows “bridge rules” between
concepts across ontology modules
• E-Connections– Connects DL modules with
special types of roles called “links”
PetOwner
Petowns
• Limitations– Expressivity– Semantic Soundness
PetAnimal
Goldfish
(onto)
(into)
CTS 2006 Las Vegas, USA. May 15, 2006 26
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
Interpretation of Importing
• Domain relations are compositional consistent: r13=r12
O r23
– Therefore domain relations are transitively reusable.
x x’
ΔI1 ΔI2
CI1 CI2
r12
ΔI3
r13 r23
x’’CI3
• Domain relation: individual correspondence between local domains
• Importing establishes one-to-one domain relations between local domains– “Copied” individuals are shared between
local domains– Ensure exact reasoning w.r.t. the
integrated ontology
CTS 2006 Las Vegas, USA. May 15, 2006 27
Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory
AnimalI
CarnivoreI
DogI
goofyI
fooI
DogI
PetIPetDogI
plutoI
eatsI
(a) (b)
1
1
1
12
2
2
2
2
2
DogFoodI 2
AnimalI2
Local Interpretation
• Semantics of foreign terms is not imported
• One term may have different local interpretations
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