SlideShare une entreprise Scribd logo
1  sur  67
Modular Ontologies: Package-based Description Logics Approach Ph.D. Preliminary Dissertation Proposal Jie Bao Artificial Intelligence Research Laboratory Computer Science Department Iowa State University  Ames, IA USA 50011 Email: baojie@cs.iastate.edu July 26, 2006
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Motivation - Sub  Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Ontologies ,[object Object],[object Object],[object Object],[object Object],Ontology  Why Modular  Considerations  Ontology Language  Modular Ontology Language
Why Ontology ? ,[object Object],[object Object],[object Object],[object Object],Ontology  Why Modular  Considerations  Ontology Language  Modular Ontology Language
Description Logics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Ontology  Why Modular  Considerations  Ontology Language  Modular Ontology Language 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) Ontology  Why Modular  Considerations  Ontology Language  Modular Ontology Language ALC
Modular Ontologies ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Ontology   Why Modular   Considerations  Ontology Language  Modular Ontology Language
Modular Ontology: Example Ontology   Why Modular   Considerations  Ontology Language  Modular Ontology Language 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 ,[object Object],[object Object],[object Object],Ontology  Why Modular  Considerations   Ontology Language  Modular Ontology Language [CTS06 Paper] a.k.a [1] ,[object Object],[object Object],[object Object]
Partial vs All-or-Nothing Reuse ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Ontology  Why Modular  Considerations   Ontology Language  Modular Ontology Language [CTS06 Paper] a.k.a [1] 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 ontology Legend :
Organizational vs Semantic Structure  Ontology  Why Modular  Considerations   Ontology Language  Modular Ontology Language [CTS06 Paper] a.k.a [1] Animal is a part of ,[object Object],[object Object],[object Object],[object Object]
Knowledge Hiding vs Sharing ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Ontology  Why Modular  Considerations   Ontology Language  Modular Ontology Language [CTS06 Paper] a.k.a [1] Locally visible : Has date Globally visible : Has activity A  schedule ontology
Ontology Languages Today XML HTML RDFS SHOE OIL DAML-ONT OWL RDF Revision Extend vocabularies Combine vocabularies Extend HTML tags for semantic description Define vocabularies SGML 1992  1998  1999  2000  2001  2002  2003 DAML (DAML+OIL) Ontology  Why Modular  Considerations  Ontology Language   Modular Ontology Language
Ontology Languages Today (2) ,[object Object],[object Object],[object Object],[object Object],Ontology  Why Modular  Considerations  Ontology Language   Modular Ontology Language
Modular Ontology Languages Today OWL 2002  2003  2004  2005  2006 C-OWL CTXWL E-Connections Our approach DDL based P-OWL (Planning) (E-connection can also work other logics e.g. modal logic) P-DL Ontology  Why Modular  Considerations  Ontology Language   Modular Ontology Language
Modular Ontology Languages Today (2) ,[object Object],[object Object],[object Object],Ontology  Why Modular  Considerations  Ontology Language   Modular Ontology Language ,[object Object],[object Object],PetOwner Pet owns ,[object Object],[object Object],Pet Animal Dog (onto) (into)
Expressivity Comparison [ASWC2006 Paper] a.k.a. [4] Ontology  Why Modular  Considerations  Ontology Language   Modular Ontology Language
Inference Difficulties ,[object Object],Ontology  Why Modular  Considerations  Ontology Language   Modular Ontology Language Pet Animal Cat Does not mean Animal Cat (Transitive reusability) Flying Penguin   ~Flying Penguin is still satisfiable (has  instance) (inter-module unsatisfiability) ,[object Object],Pet Animal X Not expressible [ASWC2006 Paper] a.k.a. [4]
Ontology Languages Needed ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Ontology  Why Modular  Considerations  Ontology Language   Modular Ontology Language
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
P-DL P 3 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 P 1 P 2 public private P 1 P 2 public private [CTS06 Paper] a.k.a [1]
Package ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Package   Package Hierarchy  Scope Limitation [CTS06 Paper] a.k.a [1] General Pet Wild Livestock Animal ontology PetDog Pet Dog General
Package: Example [CTS06 Paper] a.k.a [1] Package   Package Hierarchy  Scope Limitation O 1  (General Animal) O 2  (Pet) It uses ALCP, but not ALCP C
Nested Package  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[CTS06 Paper] a.k.a [1] Package   Package Hierarchy   Scope Limitation General Pet Dog Animal ontology
Scope Limitation Modifier   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[CTS06 Paper] a.k.a [1] Package   Package Hierarchy  Scope Limitation P 3 P 1 P 2 public private P 1 P 2 public private
SLM: example A schedule ontology Hidden: details of the activity Visible: there is an activity [CTS06 Paper] a.k.a [1] Package   Package Hierarchy  Scope Limitation
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Semantics of DL ,[object Object],[object Object],[object Object],DL Semantics   Local & Global Interpretations  Semantics of Importing 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 Interpretation - Example DL Semantics   Local & Global Interpretations  Semantics of Importing Interpretation :  In any world (or called model) that conforms to the ontology   Ontology: Dog   I Animal I ,[object Object],goofy I ,[object Object],eats I ,[object Object],DogFood I
Local Interpretations Animal I Carnivore I Dog I goofy I foo I Dog I Pet I PetDog I pluto I eats I 1 1 1 1 2 2 2 2 2 2 DogFood I 2 Animal I 2 O 1 O 2 DL Semantics  Local & Global Interpretations   Semantics of Importing [CTS06 Paper] a.k.a [1] A modular ontology may have multiple (local) interpretation for each of the package
Global Interpretations DL Semantics  Local & Global Interpretations   Semantics of Importing [CTS06 Paper] a.k.a [1] Animal I Carnivore I Dog I I PetDog I goofy I Pet I eats I g g g g g g g foo I g DogFood I g ,[object Object],[object Object],Animal I Carnivore I Dog I goofy I foo I Dog I Pet I PetDog I pluto I eats I 1 1 1 1 2 2 2 2 2 2 DogFood I 2 Animal I 2
Semantics of Importing DL Semantics  Local & Global Interpretations  Semantics of Importing [CS-TR-408] a.k.a [3] O 1 O 2 importing Animal I Carnivore I Dog I goofy I foo I Dog I Pet I PetDog I pluto I eats I 1 1 1 1 2 2 2 2 2 2 DogFood I 2 Animal I 2 domain relation
Semantics of Importing ,[object Object],[object Object],[object Object],[object Object],[object Object],DL Semantics  Local & Global Interpretations  Semantics of Importing [CS-TR-408] a.k.a [3] x x’ Δ I 1 Δ I 2 C I 1 C I 2 r 12 Δ I 3 r 13 r 23 x’’ C I 3
Partially Overlapped Model x x’ Δ I 1 Δ I 2 C I 1 C I 2 Δ I 3 r 13 r 23 x’’ C I 3 x C I DL Semantics  Local & Global Interpretations  Semantics of Importing Global interpretation obtained from local Interpretations by merging shared individuals [CS-TR-408] a.k.a [3] r 12
P-DL Semantics Features ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],DL Semantics  Local & Global Interpretations  Semantics of Importing
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Reasoning by Model Construction Tableau Algorithm   Federated Reasoning  ALCP C  Reasoning Model x Man I Human I ,[object Object],Reasoning ,[object Object],To query Man  Human ,[object Object]
Tableau Algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Tableau Algorithm   Federated Reasoning  ALCP C  Reasoning
Tableau Algorithm: Example Dog(goofy) Animal(goofy) (  eats.DogFood)(goofy) eats(goofy,foo) DogFood(foo) goofy L(goofy)={Dog, Animal,  eats.DogFood } foo L(foo)={DogFood } eats ABox Representation Completion Tree Representation Note: both representations are simplified for demostration purpose Tableau Algorithm   Federated Reasoning  ALCP C  Reasoning
Reasoning for Modular Ontology ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Tableau Algorithm   Federated Reasoning   ALCP C  Reasoning
Distributed Reasoning Chef:  Hello there, children!   Where does Kyle move to?  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. Tableau Algorithm   Federated Reasoning   ALCP C  Reasoning Stan:  Hey, Chef . Is Kyle’s new home far from us? Cartman:  San Francisco, I guess.
Federated Reasoning for P-DL ,[object Object],[object Object],[object Object],[object Object],[object Object],(1) (2) (3) (4) Tableau Algorithm   Federated Reasoning   ALCP C  Reasoning
Distributed Tableaux x 1 {A 1 } {A 2 } {A 3 } x 2 x 4 x 1 {B 1 } {B 3 } {B 2 } x 3 x 4 The (conceptual) global tableau Local Reasoner for package A Local Reasoner for package B Shared individuals mean partially overlapped local models Tableau Algorithm   Federated Reasoning   ALCP C  Reasoning [CRR06 Paper] a.k.a [6] x 1 {A 1 ,B 1 } {A 2 } {A 3 ,B 3 } {B 2 } x 2 x 3 x 4
Communication among Local Tableaux  ,[object Object],[object Object],[object Object],[object Object],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 Tableau Algorithm   Federated Reasoning   ALCP C  Reasoning [CRR06 Paper] a.k.a [6] T 1 T 2
Tableau Expansion Tableau Expansion for ALCP C  with acyclic importing Tableau Algorithm   Federated Reasoning   ALCP C  Reasoning [CRR06 Paper] a.k.a [6]
Tableau Expansion: Example ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],L 3 (x)={ A⊓  D ,   C⊔D A,  C,   D} More details see CRR 2006 paper and WI 2006 draft [5,6] Tableau Algorithm   Federated Reasoning   ALCP C  Reasoning T 3 x r(x,  C ) x x r(x,A) T 2 T 1 L 2 (x)={  B⊔C  C ,   B} L 1 (x)={  A⊔B A ,   B ,  B } r(x,  B )  (x)  (x)  (x)
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Collaborative Ontology Building ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],COB Editor  WikiOnt  INDUS [BIDM06 Paper] a.k.a [8]
The COB Editor Pig Package Cattle Package Chicken Package [BIDM06 Paper] a.k.a [8]
WikiOnt ,[object Object],[object Object],[object Object],COB Editor   WikiOnt   INDUS [EON04 Paper] a.k.a [7]
WikiOnt 2.0 (under development) COB Editor   WikiOnt   INDUS
Data Integration (INDUS) COB Editor   WikiOnt  INDUS O S D 1 D 2 D 3 M 1 M 2 M 3 View Real Data Source Mapping Data source ontologies User ontology [BIDM05 Paper] a.k.a [10]
INDUS: Query Translation ,[object Object],[object Object],[object Object],COB Editor   WikiOnt  INDUS [IJSWIS Draft] a.k.a [9]
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object]
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 Design Conceptualization  
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 INDUS Query Engine INDUS Editors Improved INDUS WI06 Paper My First Ontology Editor PDB Agent INDUS Mapping Reasoner WikiOnt 2.0 P-OWL Collabroative Ontology Building Distributed & Concealable Reasoning WikiOnt Reasoning with inconsistency BIDM 06 Paper
Schedule (Future) 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 8/1  9/1  10/1  11/1  12/1  1/1  2/1  3/1  4/1
Main Contributions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Publications (on P-DL) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],http://boole.cs.iastate.edu:9090/popeye/Wiki.jsp?page=Academic.Basic.CV.Publication
Other Publications ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],http://boole.cs.iastate.edu:9090/popeye/Wiki.jsp?page=Academic.Basic.CV.Publication
References (Related Work) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
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
[object Object]
[object Object]
Distributed Interpretations ,[object Object],[object Object],[object Object],P 1 ,P 2 DL Semantics  Local & Global Interpretations   Semantics of Importing [CTS06 Paper] a.k.a [1] A  B C  D 1 B  C C  P 2 B,C B  C 3 B,C = x x’ B I 2  = C I 2  =P I 2  A I 1  = B I 1 ,C I 1  =D I 1  = x x’ B I 3 y A I 1  = B I 1  C I 1 = D I 1  y’ C I 3 P 1 ,P 3

Contenu connexe

Tendances

Artificial intelligence and first order logic
Artificial intelligence and first order logicArtificial intelligence and first order logic
Artificial intelligence and first order logicparsa rafiq
 
Representing and Reasoning with Modular Ontologies
Representing and Reasoning with Modular OntologiesRepresenting and Reasoning with Modular Ontologies
Representing and Reasoning with Modular OntologiesJie Bao
 
Collaborative Package-based Ontology Building and Usage
Collaborative Package-based Ontology Building and UsageCollaborative Package-based Ontology Building and Usage
Collaborative Package-based Ontology Building and UsageJie Bao
 
Lean Logic for Lean Times: Varieties of Natural Logic
Lean Logic for Lean Times: Varieties of Natural LogicLean Logic for Lean Times: Varieties of Natural Logic
Lean Logic for Lean Times: Varieties of Natural LogicValeria de Paiva
 
ProLog (Artificial Intelligence) Introduction
ProLog (Artificial Intelligence) IntroductionProLog (Artificial Intelligence) Introduction
ProLog (Artificial Intelligence) Introductionwahab khan
 
Divide and Conquer Semantic Web with Modular
Divide and Conquer Semantic Web with ModularDivide and Conquer Semantic Web with Modular
Divide and Conquer Semantic Web with ModularJie Bao
 
Contexts 4 quantification (CommonSense2013)
Contexts 4 quantification (CommonSense2013)Contexts 4 quantification (CommonSense2013)
Contexts 4 quantification (CommonSense2013)Valeria de Paiva
 
Ai lecture 09(unit03)
Ai lecture  09(unit03)Ai lecture  09(unit03)
Ai lecture 09(unit03)vikas dhakane
 
DESIGN OF A RULE BASED HINDI LEMMATIZER
DESIGN OF A RULE BASED HINDI LEMMATIZERDESIGN OF A RULE BASED HINDI LEMMATIZER
DESIGN OF A RULE BASED HINDI LEMMATIZERcsandit
 
Design of a rule based hindi lemmatizer
Design of a rule based hindi lemmatizerDesign of a rule based hindi lemmatizer
Design of a rule based hindi lemmatizercsandit
 
Introduction to logic and prolog - Part 1
Introduction to logic and prolog - Part 1Introduction to logic and prolog - Part 1
Introduction to logic and prolog - Part 1Sabu Francis
 
Working with big biomedical ontologies
Working with big biomedical ontologiesWorking with big biomedical ontologies
Working with big biomedical ontologiesrobertstevens65
 
Ai lecture 10(unit03)
Ai lecture  10(unit03)Ai lecture  10(unit03)
Ai lecture 10(unit03)vikas dhakane
 
Prolog (present)
Prolog (present) Prolog (present)
Prolog (present) Melody Joey
 
MACHINE LEARNING-LEARNING RULE
MACHINE LEARNING-LEARNING RULEMACHINE LEARNING-LEARNING RULE
MACHINE LEARNING-LEARNING RULEDrBindhuM
 
Introduction to Ontology Engineering with Fluent Editor 2014
Introduction to Ontology Engineering with Fluent Editor 2014Introduction to Ontology Engineering with Fluent Editor 2014
Introduction to Ontology Engineering with Fluent Editor 2014Cognitum
 

Tendances (20)

Artificial intelligence and first order logic
Artificial intelligence and first order logicArtificial intelligence and first order logic
Artificial intelligence and first order logic
 
Representing and Reasoning with Modular Ontologies
Representing and Reasoning with Modular OntologiesRepresenting and Reasoning with Modular Ontologies
Representing and Reasoning with Modular Ontologies
 
Collaborative Package-based Ontology Building and Usage
Collaborative Package-based Ontology Building and UsageCollaborative Package-based Ontology Building and Usage
Collaborative Package-based Ontology Building and Usage
 
Lean Logic for Lean Times: Varieties of Natural Logic
Lean Logic for Lean Times: Varieties of Natural LogicLean Logic for Lean Times: Varieties of Natural Logic
Lean Logic for Lean Times: Varieties of Natural Logic
 
Icml12
Icml12Icml12
Icml12
 
ProLog (Artificial Intelligence) Introduction
ProLog (Artificial Intelligence) IntroductionProLog (Artificial Intelligence) Introduction
ProLog (Artificial Intelligence) Introduction
 
Divide and Conquer Semantic Web with Modular
Divide and Conquer Semantic Web with ModularDivide and Conquer Semantic Web with Modular
Divide and Conquer Semantic Web with Modular
 
Contexts 4 quantification (CommonSense2013)
Contexts 4 quantification (CommonSense2013)Contexts 4 quantification (CommonSense2013)
Contexts 4 quantification (CommonSense2013)
 
Ai lecture 09(unit03)
Ai lecture  09(unit03)Ai lecture  09(unit03)
Ai lecture 09(unit03)
 
DESIGN OF A RULE BASED HINDI LEMMATIZER
DESIGN OF A RULE BASED HINDI LEMMATIZERDESIGN OF A RULE BASED HINDI LEMMATIZER
DESIGN OF A RULE BASED HINDI LEMMATIZER
 
Design of a rule based hindi lemmatizer
Design of a rule based hindi lemmatizerDesign of a rule based hindi lemmatizer
Design of a rule based hindi lemmatizer
 
Prolog
PrologProlog
Prolog
 
Introduction to logic and prolog - Part 1
Introduction to logic and prolog - Part 1Introduction to logic and prolog - Part 1
Introduction to logic and prolog - Part 1
 
Working with big biomedical ontologies
Working with big biomedical ontologiesWorking with big biomedical ontologies
Working with big biomedical ontologies
 
Understanding Python
Understanding PythonUnderstanding Python
Understanding Python
 
Ai lecture 10(unit03)
Ai lecture  10(unit03)Ai lecture  10(unit03)
Ai lecture 10(unit03)
 
Prolog (present)
Prolog (present) Prolog (present)
Prolog (present)
 
MACHINE LEARNING-LEARNING RULE
MACHINE LEARNING-LEARNING RULEMACHINE LEARNING-LEARNING RULE
MACHINE LEARNING-LEARNING RULE
 
Topics Modeling
Topics ModelingTopics Modeling
Topics Modeling
 
Introduction to Ontology Engineering with Fluent Editor 2014
Introduction to Ontology Engineering with Fluent Editor 2014Introduction to Ontology Engineering with Fluent Editor 2014
Introduction to Ontology Engineering with Fluent Editor 2014
 

Similaire à Modular Ontologies: Package-based Description Logics Approach

Principles of parameters
Principles of parametersPrinciples of parameters
Principles of parametersVelnar
 
Languages, Ontologies and Automatic Grammar Generation - Prof. Pedro Rangel H...
Languages, Ontologies and Automatic Grammar Generation - Prof. Pedro Rangel H...Languages, Ontologies and Automatic Grammar Generation - Prof. Pedro Rangel H...
Languages, Ontologies and Automatic Grammar Generation - Prof. Pedro Rangel H...Facultad de Informática UCM
 
TALC 2008 Workshop 1 - Teaching and Language Corpora
TALC 2008 Workshop 1 - Teaching and Language CorporaTALC 2008 Workshop 1 - Teaching and Language Corpora
TALC 2008 Workshop 1 - Teaching and Language CorporaPascual Pérez-Paredes
 
Jarrar: ORM in Description Logic
Jarrar: ORM in Description Logic  Jarrar: ORM in Description Logic
Jarrar: ORM in Description Logic Mustafa Jarrar
 
Collaborative Ontology Building with Wiki@nt
Collaborative Ontology Building with Wiki@ntCollaborative Ontology Building with Wiki@nt
Collaborative Ontology Building with Wiki@ntJie Bao
 
Natural Language Processing
Natural Language ProcessingNatural Language Processing
Natural Language ProcessingMariana Soffer
 
Generating Lexical Information for Terminology in a Bioinformatics Ontology
Generating Lexical Information for Terminologyin a Bioinformatics OntologyGenerating Lexical Information for Terminologyin a Bioinformatics Ontology
Generating Lexical Information for Terminology in a Bioinformatics OntologyHammad Afzal
 
Language Combinatorics: A Sentence Pattern Extraction Architecture Based on C...
Language Combinatorics: A Sentence Pattern Extraction Architecture Based on C...Language Combinatorics: A Sentence Pattern Extraction Architecture Based on C...
Language Combinatorics: A Sentence Pattern Extraction Architecture Based on C...Waqas Tariq
 
Ontology and its various aspects
Ontology and its various aspectsOntology and its various aspects
Ontology and its various aspectssamhati27
 
Welcome to International Journal of Engineering Research and Development (IJERD)
Welcome to International Journal of Engineering Research and Development (IJERD)Welcome to International Journal of Engineering Research and Development (IJERD)
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
 
Communicative-discursive models and cognitive linguistics
Communicative-discursive models and cognitive linguisticsCommunicative-discursive models and cognitive linguistics
Communicative-discursive models and cognitive linguisticsalaidarindira0202
 
Extraction of common conceptual components from multiple ontologies
Extraction of common conceptual components from multiple ontologiesExtraction of common conceptual components from multiple ontologies
Extraction of common conceptual components from multiple ontologiesValentina Carriero
 
Pos Tagging for Classical Tamil Texts
Pos Tagging for Classical Tamil TextsPos Tagging for Classical Tamil Texts
Pos Tagging for Classical Tamil Textsijcnes
 

Similaire à Modular Ontologies: Package-based Description Logics Approach (20)

Using UML for Ontology construction: a case study in Agriculture
Using UML for Ontology construction: a case study in AgricultureUsing UML for Ontology construction: a case study in Agriculture
Using UML for Ontology construction: a case study in Agriculture
 
Using uml for ontology construction a case study in agriculture
Using uml for ontology construction a case study in agricultureUsing uml for ontology construction a case study in agriculture
Using uml for ontology construction a case study in agriculture
 
Principles of parameters
Principles of parametersPrinciples of parameters
Principles of parameters
 
Languages, Ontologies and Automatic Grammar Generation - Prof. Pedro Rangel H...
Languages, Ontologies and Automatic Grammar Generation - Prof. Pedro Rangel H...Languages, Ontologies and Automatic Grammar Generation - Prof. Pedro Rangel H...
Languages, Ontologies and Automatic Grammar Generation - Prof. Pedro Rangel H...
 
TALC 2008 Workshop 1 - Teaching and Language Corpora
TALC 2008 Workshop 1 - Teaching and Language CorporaTALC 2008 Workshop 1 - Teaching and Language Corpora
TALC 2008 Workshop 1 - Teaching and Language Corpora
 
Jarrar: ORM in Description Logic
Jarrar: ORM in Description Logic  Jarrar: ORM in Description Logic
Jarrar: ORM in Description Logic
 
Collaborative Ontology Building with Wiki@nt
Collaborative Ontology Building with Wiki@ntCollaborative Ontology Building with Wiki@nt
Collaborative Ontology Building with Wiki@nt
 
Natural Language Processing
Natural Language ProcessingNatural Language Processing
Natural Language Processing
 
The basics of ontologies
The basics of ontologiesThe basics of ontologies
The basics of ontologies
 
Generating Lexical Information for Terminology in a Bioinformatics Ontology
Generating Lexical Information for Terminologyin a Bioinformatics OntologyGenerating Lexical Information for Terminologyin a Bioinformatics Ontology
Generating Lexical Information for Terminology in a Bioinformatics Ontology
 
Ontology Engineering
Ontology EngineeringOntology Engineering
Ontology Engineering
 
Language Combinatorics: A Sentence Pattern Extraction Architecture Based on C...
Language Combinatorics: A Sentence Pattern Extraction Architecture Based on C...Language Combinatorics: A Sentence Pattern Extraction Architecture Based on C...
Language Combinatorics: A Sentence Pattern Extraction Architecture Based on C...
 
Ontology and its various aspects
Ontology and its various aspectsOntology and its various aspects
Ontology and its various aspects
 
Laboratory for applied ontology
Laboratory for applied ontologyLaboratory for applied ontology
Laboratory for applied ontology
 
nlp (1).pptx
nlp (1).pptxnlp (1).pptx
nlp (1).pptx
 
Ontology engineering
Ontology engineering Ontology engineering
Ontology engineering
 
Welcome to International Journal of Engineering Research and Development (IJERD)
Welcome to International Journal of Engineering Research and Development (IJERD)Welcome to International Journal of Engineering Research and Development (IJERD)
Welcome to International Journal of Engineering Research and Development (IJERD)
 
Communicative-discursive models and cognitive linguistics
Communicative-discursive models and cognitive linguisticsCommunicative-discursive models and cognitive linguistics
Communicative-discursive models and cognitive linguistics
 
Extraction of common conceptual components from multiple ontologies
Extraction of common conceptual components from multiple ontologiesExtraction of common conceptual components from multiple ontologies
Extraction of common conceptual components from multiple ontologies
 
Pos Tagging for Classical Tamil Texts
Pos Tagging for Classical Tamil TextsPos Tagging for Classical Tamil Texts
Pos Tagging for Classical Tamil Texts
 

Plus de Jie Bao

python-graph-lovestory
python-graph-lovestorypython-graph-lovestory
python-graph-lovestoryJie Bao
 
unix toolbox 中文版
unix toolbox 中文版unix toolbox 中文版
unix toolbox 中文版Jie Bao
 
unixtoolbox.book
unixtoolbox.bookunixtoolbox.book
unixtoolbox.bookJie Bao
 
Lean startup 精益创业 新创企业的成长思维
Lean startup 精益创业 新创企业的成长思维Lean startup 精益创业 新创企业的成长思维
Lean startup 精益创业 新创企业的成长思维Jie Bao
 
Towards social webtops using semantic wiki
Towards social webtops using semantic wikiTowards social webtops using semantic wiki
Towards social webtops using semantic wikiJie Bao
 
Semantic information theory in 20 minutes
Semantic information theory in 20 minutesSemantic information theory in 20 minutes
Semantic information theory in 20 minutesJie Bao
 
Towards a theory of semantic communication
Towards a theory of semantic communicationTowards a theory of semantic communication
Towards a theory of semantic communicationJie Bao
 
Expressive Query Answering For Semantic Wikis (20min)
Expressive Query Answering For  Semantic Wikis (20min)Expressive Query Answering For  Semantic Wikis (20min)
Expressive Query Answering For Semantic Wikis (20min)Jie Bao
 
Startup best practices
Startup best practicesStartup best practices
Startup best practicesJie Bao
 
Owl 2 quick reference card a4 size
Owl 2 quick reference card a4 sizeOwl 2 quick reference card a4 size
Owl 2 quick reference card a4 sizeJie Bao
 
ISWC 2010 Metadata Work Summary
ISWC 2010 Metadata Work SummaryISWC 2010 Metadata Work Summary
ISWC 2010 Metadata Work SummaryJie Bao
 
Expressive Query Answering For Semantic Wikis
Expressive Query Answering For  Semantic WikisExpressive Query Answering For  Semantic Wikis
Expressive Query Answering For Semantic WikisJie Bao
 
24 Ways to Explore ISWC 2010 Data
24 Ways to Explore ISWC 2010 Data24 Ways to Explore ISWC 2010 Data
24 Ways to Explore ISWC 2010 DataJie Bao
 
Semantic Web: In Quest for the Next Generation Killer Apps
Semantic Web: In Quest for the Next Generation Killer AppsSemantic Web: In Quest for the Next Generation Killer Apps
Semantic Web: In Quest for the Next Generation Killer AppsJie Bao
 
Representing financial reports on the semantic web a faithful translation f...
Representing financial reports on the semantic web   a faithful translation f...Representing financial reports on the semantic web   a faithful translation f...
Representing financial reports on the semantic web a faithful translation f...Jie Bao
 
XACML 3.0 (Partial) Concept Map
XACML 3.0 (Partial) Concept MapXACML 3.0 (Partial) Concept Map
XACML 3.0 (Partial) Concept MapJie Bao
 
Development of a Controlled Natural Language Interface for Semantic MediaWiki
Development of a Controlled Natural Language Interface for Semantic MediaWikiDevelopment of a Controlled Natural Language Interface for Semantic MediaWiki
Development of a Controlled Natural Language Interface for Semantic MediaWikiJie Bao
 
Digital image self-adaptive acquisition in medical x-ray imaging
Digital image self-adaptive acquisition in medical x-ray imagingDigital image self-adaptive acquisition in medical x-ray imaging
Digital image self-adaptive acquisition in medical x-ray imagingJie Bao
 
Privacy-Preserving Reasoning on the Semantic Web (Poster)
Privacy-Preserving Reasoning on the Semantic Web (Poster)Privacy-Preserving Reasoning on the Semantic Web (Poster)
Privacy-Preserving Reasoning on the Semantic Web (Poster)Jie Bao
 

Plus de Jie Bao (20)

python-graph-lovestory
python-graph-lovestorypython-graph-lovestory
python-graph-lovestory
 
unix toolbox 中文版
unix toolbox 中文版unix toolbox 中文版
unix toolbox 中文版
 
unixtoolbox.book
unixtoolbox.bookunixtoolbox.book
unixtoolbox.book
 
Lean startup 精益创业 新创企业的成长思维
Lean startup 精益创业 新创企业的成长思维Lean startup 精益创业 新创企业的成长思维
Lean startup 精益创业 新创企业的成长思维
 
Towards social webtops using semantic wiki
Towards social webtops using semantic wikiTowards social webtops using semantic wiki
Towards social webtops using semantic wiki
 
Semantic information theory in 20 minutes
Semantic information theory in 20 minutesSemantic information theory in 20 minutes
Semantic information theory in 20 minutes
 
Towards a theory of semantic communication
Towards a theory of semantic communicationTowards a theory of semantic communication
Towards a theory of semantic communication
 
Expressive Query Answering For Semantic Wikis (20min)
Expressive Query Answering For  Semantic Wikis (20min)Expressive Query Answering For  Semantic Wikis (20min)
Expressive Query Answering For Semantic Wikis (20min)
 
Startup best practices
Startup best practicesStartup best practices
Startup best practices
 
Owl 2 quick reference card a4 size
Owl 2 quick reference card a4 sizeOwl 2 quick reference card a4 size
Owl 2 quick reference card a4 size
 
ISWC 2010 Metadata Work Summary
ISWC 2010 Metadata Work SummaryISWC 2010 Metadata Work Summary
ISWC 2010 Metadata Work Summary
 
Expressive Query Answering For Semantic Wikis
Expressive Query Answering For  Semantic WikisExpressive Query Answering For  Semantic Wikis
Expressive Query Answering For Semantic Wikis
 
CV
CVCV
CV
 
24 Ways to Explore ISWC 2010 Data
24 Ways to Explore ISWC 2010 Data24 Ways to Explore ISWC 2010 Data
24 Ways to Explore ISWC 2010 Data
 
Semantic Web: In Quest for the Next Generation Killer Apps
Semantic Web: In Quest for the Next Generation Killer AppsSemantic Web: In Quest for the Next Generation Killer Apps
Semantic Web: In Quest for the Next Generation Killer Apps
 
Representing financial reports on the semantic web a faithful translation f...
Representing financial reports on the semantic web   a faithful translation f...Representing financial reports on the semantic web   a faithful translation f...
Representing financial reports on the semantic web a faithful translation f...
 
XACML 3.0 (Partial) Concept Map
XACML 3.0 (Partial) Concept MapXACML 3.0 (Partial) Concept Map
XACML 3.0 (Partial) Concept Map
 
Development of a Controlled Natural Language Interface for Semantic MediaWiki
Development of a Controlled Natural Language Interface for Semantic MediaWikiDevelopment of a Controlled Natural Language Interface for Semantic MediaWiki
Development of a Controlled Natural Language Interface for Semantic MediaWiki
 
Digital image self-adaptive acquisition in medical x-ray imaging
Digital image self-adaptive acquisition in medical x-ray imagingDigital image self-adaptive acquisition in medical x-ray imaging
Digital image self-adaptive acquisition in medical x-ray imaging
 
Privacy-Preserving Reasoning on the Semantic Web (Poster)
Privacy-Preserving Reasoning on the Semantic Web (Poster)Privacy-Preserving Reasoning on the Semantic Web (Poster)
Privacy-Preserving Reasoning on the Semantic Web (Poster)
 

Dernier

Judging the Relevance and worth of ideas part 2.pptx
Judging the Relevance  and worth of ideas part 2.pptxJudging the Relevance  and worth of ideas part 2.pptx
Judging the Relevance and worth of ideas part 2.pptxSherlyMaeNeri
 
AUDIENCE THEORY -CULTIVATION THEORY - GERBNER.pptx
AUDIENCE THEORY -CULTIVATION THEORY -  GERBNER.pptxAUDIENCE THEORY -CULTIVATION THEORY -  GERBNER.pptx
AUDIENCE THEORY -CULTIVATION THEORY - GERBNER.pptxiammrhaywood
 
Choosing the Right CBSE School A Comprehensive Guide for Parents
Choosing the Right CBSE School A Comprehensive Guide for ParentsChoosing the Right CBSE School A Comprehensive Guide for Parents
Choosing the Right CBSE School A Comprehensive Guide for Parentsnavabharathschool99
 
How to Add Barcode on PDF Report in Odoo 17
How to Add Barcode on PDF Report in Odoo 17How to Add Barcode on PDF Report in Odoo 17
How to Add Barcode on PDF Report in Odoo 17Celine George
 
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17Celine George
 
Inclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdf
Inclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdfInclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdf
Inclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdfTechSoup
 
ENGLISH 7_Q4_LESSON 2_ Employing a Variety of Strategies for Effective Interp...
ENGLISH 7_Q4_LESSON 2_ Employing a Variety of Strategies for Effective Interp...ENGLISH 7_Q4_LESSON 2_ Employing a Variety of Strategies for Effective Interp...
ENGLISH 7_Q4_LESSON 2_ Employing a Variety of Strategies for Effective Interp...JhezDiaz1
 
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptx
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptxINTRODUCTION TO CATHOLIC CHRISTOLOGY.pptx
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptxHumphrey A Beña
 
How to do quick user assign in kanban in Odoo 17 ERP
How to do quick user assign in kanban in Odoo 17 ERPHow to do quick user assign in kanban in Odoo 17 ERP
How to do quick user assign in kanban in Odoo 17 ERPCeline George
 
Transaction Management in Database Management System
Transaction Management in Database Management SystemTransaction Management in Database Management System
Transaction Management in Database Management SystemChristalin Nelson
 
Virtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdf
Virtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdfVirtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdf
Virtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdfErwinPantujan2
 
FILIPINO PSYCHology sikolohiyang pilipino
FILIPINO PSYCHology sikolohiyang pilipinoFILIPINO PSYCHology sikolohiyang pilipino
FILIPINO PSYCHology sikolohiyang pilipinojohnmickonozaleda
 
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)lakshayb543
 
Proudly South Africa powerpoint Thorisha.pptx
Proudly South Africa powerpoint Thorisha.pptxProudly South Africa powerpoint Thorisha.pptx
Proudly South Africa powerpoint Thorisha.pptxthorishapillay1
 
ISYU TUNGKOL SA SEKSWLADIDA (ISSUE ABOUT SEXUALITY
ISYU TUNGKOL SA SEKSWLADIDA (ISSUE ABOUT SEXUALITYISYU TUNGKOL SA SEKSWLADIDA (ISSUE ABOUT SEXUALITY
ISYU TUNGKOL SA SEKSWLADIDA (ISSUE ABOUT SEXUALITYKayeClaireEstoconing
 
AMERICAN LANGUAGE HUB_Level2_Student'sBook_Answerkey.pdf
AMERICAN LANGUAGE HUB_Level2_Student'sBook_Answerkey.pdfAMERICAN LANGUAGE HUB_Level2_Student'sBook_Answerkey.pdf
AMERICAN LANGUAGE HUB_Level2_Student'sBook_Answerkey.pdfphamnguyenenglishnb
 
Barangay Council for the Protection of Children (BCPC) Orientation.pptx
Barangay Council for the Protection of Children (BCPC) Orientation.pptxBarangay Council for the Protection of Children (BCPC) Orientation.pptx
Barangay Council for the Protection of Children (BCPC) Orientation.pptxCarlos105
 

Dernier (20)

Judging the Relevance and worth of ideas part 2.pptx
Judging the Relevance  and worth of ideas part 2.pptxJudging the Relevance  and worth of ideas part 2.pptx
Judging the Relevance and worth of ideas part 2.pptx
 
AUDIENCE THEORY -CULTIVATION THEORY - GERBNER.pptx
AUDIENCE THEORY -CULTIVATION THEORY -  GERBNER.pptxAUDIENCE THEORY -CULTIVATION THEORY -  GERBNER.pptx
AUDIENCE THEORY -CULTIVATION THEORY - GERBNER.pptx
 
Choosing the Right CBSE School A Comprehensive Guide for Parents
Choosing the Right CBSE School A Comprehensive Guide for ParentsChoosing the Right CBSE School A Comprehensive Guide for Parents
Choosing the Right CBSE School A Comprehensive Guide for Parents
 
How to Add Barcode on PDF Report in Odoo 17
How to Add Barcode on PDF Report in Odoo 17How to Add Barcode on PDF Report in Odoo 17
How to Add Barcode on PDF Report in Odoo 17
 
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
 
LEFT_ON_C'N_ PRELIMS_EL_DORADO_2024.pptx
LEFT_ON_C'N_ PRELIMS_EL_DORADO_2024.pptxLEFT_ON_C'N_ PRELIMS_EL_DORADO_2024.pptx
LEFT_ON_C'N_ PRELIMS_EL_DORADO_2024.pptx
 
Inclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdf
Inclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdfInclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdf
Inclusivity Essentials_ Creating Accessible Websites for Nonprofits .pdf
 
ENGLISH 7_Q4_LESSON 2_ Employing a Variety of Strategies for Effective Interp...
ENGLISH 7_Q4_LESSON 2_ Employing a Variety of Strategies for Effective Interp...ENGLISH 7_Q4_LESSON 2_ Employing a Variety of Strategies for Effective Interp...
ENGLISH 7_Q4_LESSON 2_ Employing a Variety of Strategies for Effective Interp...
 
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptx
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptxINTRODUCTION TO CATHOLIC CHRISTOLOGY.pptx
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptx
 
How to do quick user assign in kanban in Odoo 17 ERP
How to do quick user assign in kanban in Odoo 17 ERPHow to do quick user assign in kanban in Odoo 17 ERP
How to do quick user assign in kanban in Odoo 17 ERP
 
Transaction Management in Database Management System
Transaction Management in Database Management SystemTransaction Management in Database Management System
Transaction Management in Database Management System
 
Virtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdf
Virtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdfVirtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdf
Virtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdf
 
FILIPINO PSYCHology sikolohiyang pilipino
FILIPINO PSYCHology sikolohiyang pilipinoFILIPINO PSYCHology sikolohiyang pilipino
FILIPINO PSYCHology sikolohiyang pilipino
 
YOUVE GOT EMAIL_FINALS_EL_DORADO_2024.pptx
YOUVE GOT EMAIL_FINALS_EL_DORADO_2024.pptxYOUVE GOT EMAIL_FINALS_EL_DORADO_2024.pptx
YOUVE GOT EMAIL_FINALS_EL_DORADO_2024.pptx
 
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
Visit to a blind student's school🧑‍🦯🧑‍🦯(community medicine)
 
Proudly South Africa powerpoint Thorisha.pptx
Proudly South Africa powerpoint Thorisha.pptxProudly South Africa powerpoint Thorisha.pptx
Proudly South Africa powerpoint Thorisha.pptx
 
ISYU TUNGKOL SA SEKSWLADIDA (ISSUE ABOUT SEXUALITY
ISYU TUNGKOL SA SEKSWLADIDA (ISSUE ABOUT SEXUALITYISYU TUNGKOL SA SEKSWLADIDA (ISSUE ABOUT SEXUALITY
ISYU TUNGKOL SA SEKSWLADIDA (ISSUE ABOUT SEXUALITY
 
YOUVE_GOT_EMAIL_PRELIMS_EL_DORADO_2024.pptx
YOUVE_GOT_EMAIL_PRELIMS_EL_DORADO_2024.pptxYOUVE_GOT_EMAIL_PRELIMS_EL_DORADO_2024.pptx
YOUVE_GOT_EMAIL_PRELIMS_EL_DORADO_2024.pptx
 
AMERICAN LANGUAGE HUB_Level2_Student'sBook_Answerkey.pdf
AMERICAN LANGUAGE HUB_Level2_Student'sBook_Answerkey.pdfAMERICAN LANGUAGE HUB_Level2_Student'sBook_Answerkey.pdf
AMERICAN LANGUAGE HUB_Level2_Student'sBook_Answerkey.pdf
 
Barangay Council for the Protection of Children (BCPC) Orientation.pptx
Barangay Council for the Protection of Children (BCPC) Orientation.pptxBarangay Council for the Protection of Children (BCPC) Orientation.pptx
Barangay Council for the Protection of Children (BCPC) Orientation.pptx
 

Modular Ontologies: Package-based Description Logics Approach

  • 1. Modular Ontologies: Package-based Description Logics Approach Ph.D. Preliminary Dissertation Proposal Jie Bao Artificial Intelligence Research Laboratory Computer Science Department Iowa State University Ames, IA USA 50011 Email: baojie@cs.iastate.edu July 26, 2006
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7. DL Constructors and Axioms Ian Horrocks (2005) : Ontology Reasoning: the Why and the How (talk) Ontology Why Modular Considerations Ontology Language Modular Ontology Language ALC
  • 8.
  • 9. Modular Ontology: Example Ontology Why Modular Considerations Ontology Language Modular Ontology Language 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
  • 10.
  • 11.
  • 12.
  • 13.
  • 14. Ontology Languages Today XML HTML RDFS SHOE OIL DAML-ONT OWL RDF Revision Extend vocabularies Combine vocabularies Extend HTML tags for semantic description Define vocabularies SGML 1992 1998 1999 2000 2001 2002 2003 DAML (DAML+OIL) Ontology Why Modular Considerations Ontology Language Modular Ontology Language
  • 15.
  • 16. Modular Ontology Languages Today OWL 2002 2003 2004 2005 2006 C-OWL CTXWL E-Connections Our approach DDL based P-OWL (Planning) (E-connection can also work other logics e.g. modal logic) P-DL Ontology Why Modular Considerations Ontology Language Modular Ontology Language
  • 17.
  • 18. Expressivity Comparison [ASWC2006 Paper] a.k.a. [4] Ontology Why Modular Considerations Ontology Language Modular Ontology Language
  • 19.
  • 20.
  • 21.
  • 22. P-DL P 3 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 P 1 P 2 public private P 1 P 2 public private [CTS06 Paper] a.k.a [1]
  • 23.
  • 24. Package: Example [CTS06 Paper] a.k.a [1] Package Package Hierarchy Scope Limitation O 1 (General Animal) O 2 (Pet) It uses ALCP, but not ALCP C
  • 25.
  • 26.
  • 27. SLM: example A schedule ontology Hidden: details of the activity Visible: there is an activity [CTS06 Paper] a.k.a [1] Package Package Hierarchy Scope Limitation
  • 28.
  • 29.
  • 30.
  • 31. Local Interpretations Animal I Carnivore I Dog I goofy I foo I Dog I Pet I PetDog I pluto I eats I 1 1 1 1 2 2 2 2 2 2 DogFood I 2 Animal I 2 O 1 O 2 DL Semantics Local & Global Interpretations Semantics of Importing [CTS06 Paper] a.k.a [1] A modular ontology may have multiple (local) interpretation for each of the package
  • 32.
  • 33. Semantics of Importing DL Semantics Local & Global Interpretations Semantics of Importing [CS-TR-408] a.k.a [3] O 1 O 2 importing Animal I Carnivore I Dog I goofy I foo I Dog I Pet I PetDog I pluto I eats I 1 1 1 1 2 2 2 2 2 2 DogFood I 2 Animal I 2 domain relation
  • 34.
  • 35. Partially Overlapped Model x x’ Δ I 1 Δ I 2 C I 1 C I 2 Δ I 3 r 13 r 23 x’’ C I 3 x C I DL Semantics Local & Global Interpretations Semantics of Importing Global interpretation obtained from local Interpretations by merging shared individuals [CS-TR-408] a.k.a [3] r 12
  • 36.
  • 37.
  • 38.
  • 39.
  • 40. Tableau Algorithm: Example Dog(goofy) Animal(goofy) ( eats.DogFood)(goofy) eats(goofy,foo) DogFood(foo) goofy L(goofy)={Dog, Animal, eats.DogFood } foo L(foo)={DogFood } eats ABox Representation Completion Tree Representation Note: both representations are simplified for demostration purpose Tableau Algorithm Federated Reasoning ALCP C Reasoning
  • 41.
  • 42. Distributed Reasoning Chef: Hello there, children! Where does Kyle move to? 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. Tableau Algorithm Federated Reasoning ALCP C Reasoning Stan: Hey, Chef . Is Kyle’s new home far from us? Cartman: San Francisco, I guess.
  • 43.
  • 44. Distributed Tableaux x 1 {A 1 } {A 2 } {A 3 } x 2 x 4 x 1 {B 1 } {B 3 } {B 2 } x 3 x 4 The (conceptual) global tableau Local Reasoner for package A Local Reasoner for package B Shared individuals mean partially overlapped local models Tableau Algorithm Federated Reasoning ALCP C Reasoning [CRR06 Paper] a.k.a [6] x 1 {A 1 ,B 1 } {A 2 } {A 3 ,B 3 } {B 2 } x 2 x 3 x 4
  • 45.
  • 46. Tableau Expansion Tableau Expansion for ALCP C with acyclic importing Tableau Algorithm Federated Reasoning ALCP C Reasoning [CRR06 Paper] a.k.a [6]
  • 47.
  • 48.
  • 49.
  • 50. The COB Editor Pig Package Cattle Package Chicken Package [BIDM06 Paper] a.k.a [8]
  • 51.
  • 52. WikiOnt 2.0 (under development) COB Editor WikiOnt INDUS
  • 53. Data Integration (INDUS) COB Editor WikiOnt INDUS O S D 1 D 2 D 3 M 1 M 2 M 3 View Real Data Source Mapping Data source ontologies User ontology [BIDM05 Paper] a.k.a [10]
  • 54.
  • 55.
  • 56. 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 Design Conceptualization  
  • 57. 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 INDUS Query Engine INDUS Editors Improved INDUS WI06 Paper My First Ontology Editor PDB Agent INDUS Mapping Reasoner WikiOnt 2.0 P-OWL Collabroative Ontology Building Distributed & Concealable Reasoning WikiOnt Reasoning with inconsistency BIDM 06 Paper
  • 58. Schedule (Future) 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 8/1 9/1 10/1 11/1 12/1 1/1 2/1 3/1 4/1
  • 59.
  • 60.
  • 61.
  • 62.
  • 63. 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
  • 64. 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
  • 65.
  • 66.
  • 67.

Notes de l'éditeur

  1. key