Modular Ontologies: Package-based Description Logics Approach

Iowa State University. July 26, 2006 1

Iowa State University Department of Computer ScienceArtificial Intelligence Research Laboratory

Modular Ontologies: Package-based Description Logics Approach

Ph.D. Preliminary Dissertation Proposal

Jie Bao

Artificial Intelligence Research LaboratoryComputer Science Department

Iowa State University Ames, IA USA 50011

Email: [email protected]

July 26, 2006



Outline

• Motivation• Package-based Description Logics:

Language Features• Package-based Description Logics :

Semantics• Package-based Description Logics :

Reasoning• Applications• Research Plan



Motivation - Sub Outline

• Ontology – why and what

• Modular Ontology– Why

– Key Considerations

• Representing Ontology– Ontology Languages

– Modular Ontology Languages



Ontologies

What is ontology?

• (in philosophy) the study of being [Aristotle]

• (in formal computer science setting) the shared specification of conceptualization [Gruber 1993]

• (in an informal way) a term set and relations between terms

Ontology Why Modular Considerations Ontology Language Modular Ontology Language



Why Ontology ?

• To classify things, e.g. categories of life

• To precisely annotate data, e.g. library book topic catalog

• To infer hidden knowledge from existing knowledge, e.g. Dogs are Mammals, Mammals are Animal, so Dogs are Animals

• To share knowledge unambiguously (ontological commitment), e.g. is a mouse an animal or a part of a computer ?




Description Logics

• Description Logics (DL): a knowledge representation formalism to describe ontologies

• DL is the foundation for ontology languages, e.g., OWL

• Ontology example– Dog is Animal– some Dog eats DogFood– goofy is-a Dog


concept

role

individual

axioms terms

Terminology or TBox

Assertions or ABox (facts)



DL Constructors and Axioms

Ian Horrocks (2005) : Ontology Reasoning: the Why and the How (talk)


ALC



Modular Ontologies

• What is modular ontology?– An ontology that is composed by a set of smaller

(semantically) connected component ontologies

• Why modular ontology ?– Collaborative Ontology Building– Selective Ontology Reuse– Selective Knowledge Hiding– Distributed Data Management– Large Ontology Storage and Reasoning




Modular Ontology: Example

Swine

Cattle Chicken

Horse

Each group works on an ontology module for a particular species (according to the group’s best expertise)


Collaborative building of an animal trait ontology that involves multiple research groups across the world



Local vs Global Semantics

• Localizing knowledge is helpful to – reduce risk of global semantic conflicts– reduce ontology engineering complexity (divide and

conquer)


• Ontologies represent local views of its producers – Biologist: dog species only eats animal

– Pet owner: pet dog sometimes eats DogFood, which is not only animal

[CTS06 Paper] a.k.a [1]



Partial vs All-or-Nothing Reuse

General Pet

Wild Livestock

Animal Ontology(Centralized)

MyPet

General

Pet

Wild

Livestock

MyPet

Animal Ontology(Package-extended)

Semantic importing

Knowledge incorporated in MyPet ontology

Knowledge 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!





Organizational vs Semantic Structure

Animal

is a part of

• Semantic structure: how to relate meanings of terms– Eg: ‘Mouse’ is a kind of ‘Animal’ or

‘Mouse’ is part of ‘Computer’

• Organizational structure: how to arrange terms for better usage and understanding– Eg: Computer Science Dictionary and

Biology Dictionary





Knowledge Hiding vs Sharing

• Ontology reflects shared knowledge in general Locally visible:

Has dateGlobally visible:

Has activity

A schedule ontology

• However, the provider may also wish to hide part of it. – Privacy, Copyright, Security

• In addition, partial hiding helps for safer ontology organization– Reduce unexpected interactions– Separate “details” and “interface”





Ontology Languages Today

XMLXML

HTMLHTML

RDFSRDFS

SHOESHOE

OILOIL

DAML-ONTDAML-ONT

OWLOWLRDFRDF

Revision

Extendvocabularies

Combinevocabularies

Extend HTML tagsfor semantic description

Define vocabularies

SGMLSGML

1992 1998 1999 2000 2001 2002 2003

DAML(DAML+OIL)

DAML(DAML+OIL)




Ontology Languages Today (2)

• 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




Modular Ontology Languages Today

OWL

2002 2003 2004 2005 2006

C-OWLC-OWLCTXWL

E-ConnectionsE-Connections

Our approach

DDL based

P-OWLP-OWL

(Planning)

(E-connection can also work other logics e.g. modal logic)

P-DL




Modular Ontology Languages Today (2)

• E-Connections [19,20]

– Connects DL modules with special types of roles called “links”

PetOwner

Petowns

• Limitations [4]

– Expressivity– Inference Diffculties

• Distributed Description Logics (DDL) [14] & C-OWL[15]

– Allows “bridge rules” between concepts across ontology modules

PetAnimal

Dog

(onto)

(into)




Expressivity Comparison

[ASWC2006 Paper] a.k.a. [4]




Inference Difficulties

• DDL


PetAnimal Cat

Does not mean Animal Cat

(Transitive reusability)

Flying

Penguin ~Flying

Penguin is still satisfiable (has instance)(inter-module unsatisfiability)

• E-Connections

PetAnimalX

Not expressible[ASWC2006 Paper] a.k.a. [4]



Ontology Languages Needed

• Modularity– Has localized semantics– Allows partial ontology reuse– Utilizes organizational and semantic structure – Enables collaborative and scalable tools

• Knowledge Hiding– Builds safer ontologies– Reduces unwanted interactions– Hides details (encapsulate semantics)




Outline

• Motivation• Package-based Description Logics: Language

Features– Package– Package Hierarchy– Scope Limitation Modifier

• Package-based Description Logics: Semantics• Package-based Description Logics : Reasoning• Applications• Research Plan



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




Package• A package is an ontology module

with clearly defined access interface;• Each package is defined with certain

ontology language– Each term has a home package

• A package can imports terms from other packages

• Package extension is denoted as P– Package extension with only concept

name importing is denoted as PC

– E.g., ALCPC = ALC + PC

General Pet

Wild Livestock

Animal ontology

PetDogPet

DogGeneral

Package Package Hierarchy Scope Limitation




Package: Example

O1 (General Animal) O2 (Pet)

It uses ALCP, but not ALCPC





Nested Package

• A nested package is a 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

General

Pet

Dog

Animal ontology





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

– 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

P3

P1

P2

public

private

P1

P2

public

private





SLM: exampleA schedule ontology

Hidden: details of the activity

Visible: there is an activity





Outline


Features• Package-based Description Logics : Semantics

– DL Semantics– Local Interpretation and Global Interpretation– Semantics of Importing

• Package-based Description Logics : Reasoning• Applications• Research Plan



Semantics of DL

• Clear and unambiguous semantics is the prerequisite for reasoning

• Semantics: meaning of language forms. • DL usually has model-theoretical semantics

Syntax Semantics

Man Human

In any world (also called an interpretation), anybody who is a Man is also a Human

{x|Man(x)} {x|Human(x)}

DL Semantics Local & Global Interpretations Semantics of Importing



DL Interpretation - Example

Interpretation: In any world (or called model) that conforms to the ontology

Ontology:

Dog I

AnimalI

• For any instance x of Dog, x is also an instance of Animal.

goofyI

• The individual goofy in the world is a Dog.

eatsI

• There is a y in the world, that a Dog x eats y and y is a DogFood

DogFoodI




Local Interpretations

AnimalI

CarnivoreI

DogI

goofyI

fooI

DogI

PetIPetDogI

plutoI

eatsI

1

1

1

12

2

2

2

2

2

DogFoodI 2

AnimalI2

O1 O2



A modular ontology may have multiple (local) interpretation for each of the package



Global Interpretations

AnimalI

CarnivoreI

DogI

I

PetDogI

goofyI

PetI

eatsI

g

g

g

g

g

g

g

fooIg

DogFoodI g

• The global interpretation for a conceptually integrated ontology• It can be combined from local interpretations

AnimalI

CarnivoreI

DogI

goofyI

fooI

DogI

PetIPetDogI

plutoI

eatsI

1

1

1

12

2

2

2

2

2

DogFoodI 2

AnimalI2





Semantics of ImportingDL Semantics Local & Global Interpretations Semantics of Importing

O1 O2importing

AnimalI

CarnivoreI

DogI

goofyI fooI

DogIPetIPetDogI

plutoI

eatsI

1

1

1

1

2

2

2

2

2

2

DogFoodI 2

AnimalI2

domain relation[CS-TR-408] a.k.a [3]



Semantics of Importing

• Domain relations are compositionally 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 – “Copied” individuals are

shared


[CS-TR-408] a.k.a [3]



Partially Overlapped Model

x x’

ΔI1 ΔI2

CI1 CI2

r12

ΔI3

r13 r23

x’’CI3

x

CI


Global interpretation obtained from localInterpretations by merging shared individuals

[CS-TR-408] a.k.a [3]



P-DL Semantics Features

• Localized Semantics• Decidable (when all modules are from the

same decidable DL)• Stronger expressivity (≈ DDL + E-

Connections)• Solving reasoning diffculities in other

approaches– intermodule unsatisfiability– module transitive reusability




Outline


Features• Package-based Description Logics : Semantics• Package-based Description Logics : Reasoning

– Tableau Algorithm– Federated Reasoning: Basic Idea– Distributed Tableau Algorithm for ALCPC

• Applications• Research Plan



Model

x

ManI

HumanI

2. If such a model is not possible in any situation, Man <= Human is true

Reasoning by Model ConstructionReasoning

1. Suppose it is not true, then at least one individual x in a world (model) is Man but not Human

To query

Man Human

Tableau Algorithm Federated Reasoning ALCPC Reasoning

3. If such a model can be constructed, then Man <= Human is not true



Tableau Algorithm

• Description Logics usually uses the Tableau Algorithm [Badder & Sattler 2001] for reasoning tasks.

• A tableau is a representation of a model• Basic idea:

– start with some initial facts for an ontology– use some rules (called tableau expansion rules) to

infer new facts, • until no rule can be applied, or inconsistencies are found

among those facts.

– If a clash-free fact set is found, a model of the ontology is constructed




Tableau Algorithm: Example

Dog(goofy)

Animal(goofy)( eats.DogFood)(goofy)

eats(goofy,foo)DogFood(foo)

goofyL(goofy)={Dog, Animal, eats.DogFood }

fooL(foo)={DogFood }

eats

ABox Representation Completion Tree Representation

Note: both representations are simplified for demostration purpose




Reasoning for Modular Ontology

• Major Consideration: should not require the integration of ontology modules.– High communication cost– High local memory cost– May violate module autonomy, e.g., privacy

• Question: can we do reasoning for P-DL without – (syntactic level) an integrated ontology ?– (semantic level) a (materialized) global tableau ?




Distributed Reasoning

Stan: Hey, Chef. Is Kyle’s new home far from us?

Chef: Hello there, children! Where does Kyle move to?

Cartman: San Francisco, I guess.

Chef: We are in South Park, Colorado; San Francisco is in California; Colorado is far from California.

Stan: So they are far from us. Too Bad.




Federated Reasoning for P-DL

Basic strategy• Use multiple local reasoners, each

for a single package• Each local reasoner creates and

mainteins a local tableau based on local knowledge

• A local reasoner may query other reasoners if its local knowledge is incomplete

• Global relation among tableaux is created by messages

(1)

(2)(3)

(4)




Distributed Tableaux

x1

{A1,B1}

{A2}

{A3,B3}

{B2}x2 x3

x4

x1

{A1}

{A2}

{A3}

x2

x4

x1

{B1}

{B3}

{B2}x3

x4

The (conceptual) global tableau Local Reasoner

for package ALocal Reasonerfor package B

Shared individuals mean partially overlapped local models


[CRR06 Paper] a.k.a [6]



Communication among Local Tableaux

• Membership m(y,C):

• Reporting r(y,C):

• Clash bottom(y):

• Model top(y):

y y{C?}

y y{C}

C(y)

y y{…}

y y{…}

X

Query if y is an instance of C

Notify that y is an instance of C

Notify that y has local inconsistency

Notify that no more rule can be applied locally on y



T1 T2



Tableau Expansion

Tableau Expansion for ALCPC with acyclic importingTableau Algorithm Federated Reasoning ALCPC Reasoning




T3

x

Tableau Expansion: Example

• P1: 1:A 1:B• P2: 1:B 2:C• P3: 2:C 3:D• Query: if A D (from

the point of view of P3) (it is not answerable by either DDL nor E-

Connection in their current forms)

• Reasoning: if A D is not true, then there will be clash. Hence, it must be true

L3(x)={A⊓

D, C D⊔A,C, D}

r(x,C)

x x

r(x,A)

T2 T1

L2(x)={B C⊔C, B}

L1(x)={A B⊔A, B, B}

r(x,B)

(x)

(x) (x)

More details see CRR 2006 paper and WI 2006 draft [5,6]




Outline

• Motivation• Package-based Description Logics : Language

Features• Package-based Description Logics : Semantics• Package-based Description Logics : Reasoning• Applications

– Collaborative Ontology Building (COB Editor & WikiOnt)

– Semantic Data Integration (INDUS)

• Research Plan



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

COB Editor WikiOnt INDUS

[BIDM06 Paper] a.k.a [8]


Iowa State University Department of Computer ScienceArtificial Intelligence Research LaboratoryThe COB Editor

Pig Package

Cattle Package

Chicken Package

[BIDM06 Paper] a.k.a [8]



WikiOnt

• A web browser based ontology editor

• Using Wiki script to store ontologies

• With features to support team work, version control, page locking, and navigation.


[EON04 Paper] a.k.a [7]



WikiOnt 2.0 (under development)COB Editor WikiOnt INDUS



Data Integration (INDUS)COB Editor WikiOnt INDUS

D

O

S

D

O

S

D

O

S

D

O

S

D

O

S

D

O

S

OS

D1 D2 D3

M1 M2 M3

View

Real Data Source

Mapping

Data sourceontologies

User ontology [BIDM05 Paper] a.k.a [10]



INDUS: Query Translation• Query composed using an ontology:

SELECT name, ageFROM peopleWHERE status <= O1:Graduate

• To be translated into other ontology (via ontology reasoning)

SELECT name, ageFROM people WHERE status <= O2:PhDStudent

• A query engine for restricted forms of ontologies (hierarchies) implemented in INDUS


[IJSWIS Draft] a.k.a [9]



Outline

• Motivation

• Package-based Description Logics : Language Features

• Package-based Description Logics : Semantics

• Applications

• Research Plan



Progress

Wiki@nt 2.0

Wiki@nt 1.0

INDUS

COB-Editor

Applications

Concealable Reasoning (optional)

Distributed Reasoning

Reasoning

P-OWL

PPO

Semantics of P-DL

Basic Package-based Ontologies

Language Specification

Implementation/ Specification

DesignConceptualization



Time line (past)

2003

08 09 10 11 12 01 02 03 04 05 06 07 08 09 10 11 12

2004

01 02 03 04 05 06 07 08 09 10 11 12 01 02 03 04 05 06 07 2005 2006

IKE04 Paper

ASWC04 Paper

CTS06 Paper

CRR06 Paper

COB Editor

EON04 Paper

INDUSQuery Engine

INDUSEditors

ImprovedINDUS

WI06 Paper

My First Ontology Editor

PDB Agent

INDUSMapping Reasoner

WikiOnt 2.0

P-OWL

Collabroative Ontology Building

Distributed &

Concealable Reasoning

WikiOnt

Reasoning with

inconsistency

BIDM 06 Paper



Schedule (Future)

8/1 9/1 10/1 11/1 12/1 1/1 2/1 3/1 4/1

P-OWL and PPO

Reasoner Implementation

2006 ASWC

WikiOnt 2.0 Implementation

Connect INDUS to reasoners

2006 ISWC

Dissertation Writing

2006 WI Final Defense

2006 2007



Main Contributions

• Investigate the requirement and formal semantics of modular ontologies

• Present a formal modular ontology language, P-DL, that can overcome many limitations in existing approaches– Stronger expressivity– Solve many inference difficulties

• Design a federated reasoning algorithm for P-DL that can – strictly avoid integration of ontology modules– handle reasoning tasks not solvable in existing approaches

• Apply the notion of modular ontology in collaborative ontology building and provide the first tool on this problem



Publications (on P-DL)Language Features1. J. Bao, D. Caragea, and V. Honavar. Towards collaborative environments for ontology construction and

sharing. In International Symposium on Collaborative Technologies and Systems (CTS 2006). 2006.2. J. Bao and V. Honavar. Collaborative package-based ontology building and usage. In IEEE Workshop

on Knowledge Acquisition from Distributed, Autonomous, Semantically Heterogeneous Data and Knowledge Sources, in ICDM2005. 2005.

Semantics

3. J. Bao, D. Caragea, and V. Honavar. On the semantics of linking and importing in modular ontologies (extended version). Technical report, TR-408 Computer Sicence, Iowa State University, 2006.

4. J. Bao, D. Caragea, and V. Honavar. Modular ontologies - a formal investigation of semantics and expressivity. 2006. In the Asian Semantic Web Conference (ASWC2006) (In Press).

Reasoning5. J. Bao, D. Caragea, and V. Honavar. A tableau-based federated reasoning algorithm for modular

ontologies. Submitted to 2006 IEEE/WIC/ACM International Conference on Web Intelligence, 2006 (under reviewing)

6. J. Bao, D. Caragea, and V. Honavar. A distributed tableau algorithm for package-based description logics. In the 2nd International Workshop On Context Representation And Reasoning (CRR 2006) (In Press). 2006.

Collaborative Ontology Building7. J. Bao and V. Honavar. Collaborative ontology building with wiki@nt - a multi-agent based ontology

building environment. In Proc. of 3rd International Workshop on Evaluation of Ontology-based Tools, at ISWC 2004, pages 37–46, 2004.

8. J. Bao, Z. Hu, D. Caragea, J. Reecy, and V. G. Honavar. Developing frameworks and tools for collaborative building of large biological ontologies. In The 4th International Workshop on Biological Data Management (BIDM’06). 2006 (In Press).

http://boole.cs.iastate.edu:9090/popeye/Wiki.jsp?page=Academic.Basic.CV.Publication



Other PublicationsOntology-based Data Integration (a.k.a. on INDUS project)9. J. Bao, J. Pathak, D. Caragea, N. Koul, and V. Honavar. Query translation for ontology-

extended data sources with heterogenous content ontologies. To be submitted to the International Journal on Semantic Web and Information Systems. 2006.

10. D. Caragea, J. Bao, J. Pathak, A. Silvescu, C. M. Andorf, D. Dobbs, and V. Honavar. Information integration from semantically heterogeneous biological data sources. In Proceedings of the 3rd International Workshop on Biological Data Management (BIDM'05) at DEXA 2005, pages 580-584, 2005.

11. D. Caragea, J. Zhang, J. Bao, J. Pathak, and V. Honavar. Algorithms and software for collaborative discovery from autonomous, semantically heterogeneous, distributed information sources. In ALT, pages 13-44, 2005.

12. D. Caragea, J. Pathak, J. Bao, A. Silvescu, C. M. Andorf, D. Dobbs, and V. Honavar. Information integration and knowledge acquisition from semantically heterogeneous biological data sources. In Proceedings of the 2nd International Workshop on Data Integration in Life Sciences (DILS'05), San Diego, CA, pages 175-190, 2005

Ontology Building13. J. Bao, Y. Cao, W. Tavanapong, and V. Honavar. Integration of domain-specific and

domain-independent ontologies for colonoscopy video database annotation. In Proceedings of 2004 International Conference on Information and Knowledge Engineering (IKE 04),pages 82-88. 2004.

http://boole.cs.iastate.edu:9090/popeye/Wiki.jsp?page=Academic.Basic.CV.Publication



References (Related Work)DDL:14. A. Borgida and L. Serafini. Distributed description logics: Directed domain

correspondences in federated information sources. InCoopIS/DOA/ODBASE, pages 36-53, 2002.

15. P. Bouquet, F. Giunchiglia, and F. van Harmelen. C-OWL: Contextualizing ontologies. In Second International Semantic Web Conference, volume 2870 of Lecture Notes in Computer Science, pages 164-179. Springer Verlag, 2003.

16. L. Serafini, A. Borgida, and A. Tamilin. Aspects of distributed and modular ontology reasoning. In IJCAI, pages 570-575, 2005

17. L. Serafini and A. Tamilin. Local tableaux for reasoning in distributed description logics. In Description Logics Workshop 2004, CEUR-WS Vol 104, 2004.

18. L. Serafini and A. Tamilin. Drago: Distributed reasoning architecture for the semantic web. In ESWC, pages 361-376, 2005.

E-Connections:19. B. C. Grau. Combination and Integration of Ontologies on the Semantic Web. PhD

thesis, Dpto. de Informatica, Universitat de Valencia, Spain, 2005.20. O. Kutz, C. Lutz, F. Wolter, and M. Zakharyaschev. E-connections of abstract

description systems. Artif. Intell., 156(1):1-73, 2004.



Dr. D. Caragea

J. Pathak

Dr. J. Zhang

Dr. C. Yan

D-K. Kang

Dr. V. Honavar

Y. Cao

Dr. W. Tavanapong

Dr. Z-L. Hu Dr. J. Reecy

N. Koul P. Wong

Dr. D. Dobbs

Dr. G. Leavens

Acknowledgements



Dr. D. Caragea

J. Pathak

Dr. J. Zhang

Dr. C. Yan

D-K. Kang

Dr. V. Honavar

Y. Cao

Dr. W. Tavanapong

Dr. Z-L. Hu Dr. J. Reecy

N. Koul P. Wong

Dr. D. Dobbs

Dr. G. Leavens



Thanks!



Backup



Distributed Interpretations

• Global interpretations may not exist for all packages

• Distributed interpretations may still exist for selected sets of packages.

• Thus, localized semantics helps to reduce the risk of inconsistency

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



Education

Modular Ontologies: Package-based Description Logics Approach