SlideShare uma empresa Scribd logo
1 de 13
Baixar para ler offline
‫أكاديمية الحكومة اإللكترونية الفلسطينية‬
           The Palestinian eGovernment Academy
                      www.egovacademy.ps



Tutorial 4: Ontology Engineering & Lexical Semantics

                      Session 6.1
   Ontology Engineering Challenges


                  Dr. Mustafa Jarrar
                     University of Birzeit
                     mjarrar@birzeit.edu
                       www.jarrar.info

                         PalGov © 2011                 1
About

This tutorial is part of the PalGov project, funded by the TEMPUS IV program of the
Commission of the European Communities, grant agreement 511159-TEMPUS-1-
2010-1-PS-TEMPUS-JPHES. The project website: www.egovacademy.ps
Project Consortium:
             Birzeit University, Palestine
                                                           University of Trento, Italy
             (Coordinator )


             Palestine Polytechnic University, Palestine   Vrije Universiteit Brussel, Belgium


             Palestine Technical University, Palestine
                                                           Université de Savoie, France

             Ministry of Telecom and IT, Palestine
                                                           University of Namur, Belgium
             Ministry of Interior, Palestine
                                                           TrueTrust, UK
             Ministry of Local Government, Palestine


Coordinator:
Dr. Mustafa Jarrar
Birzeit University, P.O.Box 14- Birzeit, Palestine
Telfax:+972 2 2982935 mjarrar@birzeit.eduPalGov © 2011
                                                                                                 2
© Copyright Notes
Everyone is encouraged to use this material, or part of it, but should
properly cite the project (logo and website), and the author of that part.


No part of this tutorial may be reproduced or modified in any form or by
any means, without prior written permission from the project, who have
the full copyrights on the material.




                 Attribution-NonCommercial-ShareAlike
                              CC-BY-NC-SA

This license lets others remix, tweak, and build upon your work non-
commercially, as long as they credit you and license their new creations
under the identical terms.

                                 PalGov © 2011                               3
Tutorial Map

                                                                                        Topic                          Time
                                                                  Session 1_1: The Need for Sharing Semantics          1.5
                                                                  Session 1_2: What is an ontology                     1.5
         Intended Learning Objectives
A: Knowledge and Understanding                                    Session 2: Lab- Build a Population Ontology          3
 4a1: Demonstrate knowledge of what is an ontology,               Session 3: Lab- Build a BankCustomer Ontology        3
    how it is built, and what it is used for.                     Session 4: Lab- Build a BankCustomer Ontology        3
 4a2: Demonstrate knowledge of ontology engineering
    and evaluation.                                               Session 5: Lab- Ontology Tools                       3
 4a3: Describe the difference between an ontology and a           Session 6_1: Ontology Engineering Challenges         1.5
    schema, and an ontology and a dictionary.
                                                                  Session 6_2: Ontology Double Articulation            1.5
 4a4: Explain the concept of language ontologies, lexical
    semantics and multilingualism.                                Session 7: Lab - Build a Legal-Person Ontology       3
B: Intellectual Skills                                            Session 8_1: Ontology Modeling Challenges            1.5
 4b1: Develop quality ontologies.                                 Session 8_2: Stepwise Methodologies                  1.5
 4b2: Tackle ontology engineering challenges.
 4b3: Develop multilingual ontologies.                            Session 9: Lab - Build a Legal-Person Ontology       3
 4b4: Formulate quality glosses.                                  Session 10: Zinnar – The Palestinian eGovernment     3
C: Professional and Practical Skills                              Interoperability Framework
 4c1: Use ontology tools.                                         Session 11: Lab- Using Zinnar in web services        3
 4c2: (Re)use existing Language ontologies.
                                                                  Session 12_1: Lexical Semantics and Multilingually   1.5
D: General and Transferable Skills
 d1: Working with team.                                           Session 12_2: WordNets                               1.5
 d2: Presenting and defending ideas.                              Session 13: ArabicOntology                           3
 d3: Use of creativity and innovation in problem solving.
                                                                  Session 14: Lab-Using Linguistic Ontologies          3
 d4: Develop communication skills and logical reasoning
    abilities.                                                    Session 15: Lab-Using Linguistic Ontologies          3


                                                            PalGov © 2011                                                     4
Outline and Session ILOs


This session will help students to:

4a2: Demonstrate knowledge of ontology engineering and evaluation.

4b2: Tackle ontology engineering challenges.




                           PalGov © 2011                         5
Ontology Engineering Challenges



     Ontology Usability versus Ontology Reusability

     Ontology Application Dependence




• Only these challenges will be discussed, but there are many other
  challenges that may face an ontology engineer.

• Discussing such challenges will help improve the modeling skills of
  an ontology engineer.



                             PalGov © 2011                          6
Ontology Reusability vs Usability
              Given 4 different LegalPerson ontologies (which is more usable/reusable?)

                      O1
                                                 Used by App1, 9000 times/day.

                                                 Used by App1, 1000 times/day.
                      O2
                                                 Used by App2, 1000 times/day.
                                                 Used by App1, 100 times/day.
                      O3
                                                 Used by App2, 100 times/day.
                                                 Used by App3, 100 times/day.
                                                     Used by App1, 10 times/day.
                      O4                             Used by App2, 10 times/day.
                                                     Used by App3, 10 times/day.
                                                     Used by App4, 10000 times/day.
App1:   Ministries’ Web Service to exchange companies’ profiles is based on this ontology.
App2:   Champers of commerce’s Web Service to exchange companies’ profiles, based on this ontology.
App3:   Banks designed their “new account” form, based on the company properties in this ontology (off time use).
App4:   Lawyers refer to the definition of “company”, as stated in this ontology (off time use).
                                                     PalGov © 2011                                           7
Ontology Reusability vs Usability
              Given 4 different LegalPerson ontologies (which is more usable/reusable?)

                    O1
                                     Used of different times/day.
            Usability: maximizing the numberby App1, 9000 applications using
            an ontology for the same kind of task.
                                      Used by App1, 1000 times/day.
            Reusability: maximizing the number of different applications using
                   O2
                                      Used by App2, 1000 times/day.
            an ontology over different kind of tasks.
            Why Reusability:            Used by App1, 100 times/day.
            1) Saving time, cost, and efforts…
                  O3
                                        Used by App2, 100 times/day.
            2) Increasing reliability: the more reused the more tested.
            3) An important quality  Used by App3, 100 times/day.
                                        factor: a highly reusable ontology is an
               indication that it is a good ontology.
                                        Used by App1, 10 times/day.
           How to increase Usability? Used by App2, 10 times/day.
                                             
                    O4
              by being closer to the application specifications and
              requirements at hand.
                                              Used by App3, 10 times/day.
                                             
           How to increase Reusability?Used by App4, 10 times/day.
App1: Ministries Web Service to exchange companies profiles is based on this ontology. i.e. be more
              by taking into account different usages/applications,
App2: Champers of commerce’s Web Service to exchange companies profiles, based on this ontology.
              general.
App3: Banks designed their “new account” form, based on the company properties in this ontology (off time use).
App4: Lawyers refer to the definition of “company”, as stated in this ontology (off time use).
                                                   PalGov © 2011                                          8
Ontology Reusability vs Usability
              Given 4 different LegalPerson ontologies (which is more usable/reusable?)

                    O1
                                     Used of different times/day.
            Usability: maximizing the numberby App1, 9000 applications using
            an ontology for the same kind of task.
                                      Used by App1, 1000 times/day.
            Reusability: maximizing the number of different applications using
                   O2
            an ontology over Reusability Used by App2, 1000 times/day.
                                                Usability
                             different kind of tasks.
           Why Reusability: usability and reusability 100 times/day.
           Tradeoff between                   Used by App1,
           1) Savings in time, cost, and efforts…
                     O3
                                              Used by App2, 100 times/day.
           2) The more an ontology is more reusedless more tested. be,
                  Increasing reliability: the usable the the reusable it will
           3) and important quality  Used by App3, 100 times/day.
                  An vice versa.             factor: a highly resalable ontology is an
               A good ontologyis a good ontology.
                  indication that it engineer knowsApp1, 10 times/day.
                                              Used by how/where to compromise
                 this tradeoff.
           How to increase Usability? Used by App2, 10 times/day.
                                             
                     O4
           by being closes to the application specifics and requirements at
           hand.
                                              Used by App3, 10 times/day.
                                             
           How to increase Reusability?Used by App4, 10 times/day.
App1: Ministries Web Service to exchange companies profiles is based on this ontology. i.e. be more
            by taking into account different usages/applications,
App2: Champers of commerce’s Web Service to exchange companies profiles, based on this ontology.
           general.
App3: Banks designed their “new account” form, based on the company properties in this ontology (off time use).
App4: Lawyers refer to the definition of “company”, as stated in this ontology (off time use).
                                                   PalGov © 2011                                          9
Ontology Application Dependence

  Ontologies are supposed to capture knowledge at the domain level
  independently of application requirements [G97] [GB99] [CJB99].


The problem is that when building an ontology, there will always be
intended or expected usability requirements -“at hand”- which influence
the independency level of ontology axioms.


This problem is as the Interaction Problem:
      “Representing knowledge for the purpose of solving some
      problem is strongly affected by the nature of the problem and
      the inference strategy to be applied to the problem.”
                                               Bylander and Chandrasekaran in [BC88]




                               PalGov © 2011                                           10
Ontology Application Dependence

         What is the meaning of a “book” here?




                                                                          Applications
                                                                          Bookstores
 ?




                                                                               Applications
                                                                               Library
 Usability perspectives lead to different (and sometimes conflicting)
  axiomatizations although these axiomatizations might agree at the domain level.
                                   PalGov © 2011                                         11
Ontology Application Dependence

         What is the meaning of a “book” here?




                                                                          Applications
                                                                          Bookstores
    Both are not ontologies, they are data schemes.
 ? Can you build a useful and an application-independent ontology?




                                                                               Applications
                                                                               Library
 Usability perspectives lead to different (and sometimes conflicting)
  axiomatizations although these axiomatizations might agree at the domain level.
                                   PalGov © 2011                                         12
References

Mustafa Jarrar: Towards methodological principles for ontology engineering. PhD
  Thesis. Vrije Universiteit Brussel. (May 2005)

Thomas R. Gruber: Toward Principles for the Design of Ontologies Used for Knowledge
  Sharing http://tomgruber.org/writing/onto-design.pdf

Nicola Guarino: Formal Ontology and Information Systems http://www.loa-
   cnr.it/Papers/FOIS98.pdf

Guarino, N.: Understanding, building, and using ontologies: A commentary to “Using
  Explicit Ontologies in KBS Development”, by van Heijst, Schreiber, and Wielinga."
  International Journal of Human and Computer Studies No. 46. (1997) pp. 293–310

[HV93] Hemmann, T., Voss, H.: A Reusable and Specializable Interpretation Model for
   ModelBased Diagnosis. In: Luckenhoff, C., Fensel, D., Studer, D. (eds.): Proceeding
   3rd KADS Meeting Siemens AG. Munich. March (1993) pp. 189–205




                                     PalGov © 2011                                       13

Mais conteúdo relacionado

Mais procurados

Pal gov.tutorial4.session13.arabicontology
Pal gov.tutorial4.session13.arabicontologyPal gov.tutorial4.session13.arabicontology
Pal gov.tutorial4.session13.arabicontologyMustafa Jarrar
 
Pal gov.tutorial4.session11.lab zinnarontologybasedwebservices
Pal gov.tutorial4.session11.lab zinnarontologybasedwebservicesPal gov.tutorial4.session11.lab zinnarontologybasedwebservices
Pal gov.tutorial4.session11.lab zinnarontologybasedwebservicesMustafa Jarrar
 
Pal gov.tutorial4.session7.lab legalpersonontology
Pal gov.tutorial4.session7.lab legalpersonontologyPal gov.tutorial4.session7.lab legalpersonontology
Pal gov.tutorial4.session7.lab legalpersonontologyMustafa Jarrar
 
Pal gov.tutorial4.outline
Pal gov.tutorial4.outlinePal gov.tutorial4.outline
Pal gov.tutorial4.outlineMustafa Jarrar
 
Pal gov.tutorial4.session6 2.knowledge double-articulation
Pal gov.tutorial4.session6 2.knowledge double-articulationPal gov.tutorial4.session6 2.knowledge double-articulation
Pal gov.tutorial4.session6 2.knowledge double-articulationMustafa Jarrar
 
Pal gov.tutorial4.session1 1.needforsharedsemantics
Pal gov.tutorial4.session1 1.needforsharedsemanticsPal gov.tutorial4.session1 1.needforsharedsemantics
Pal gov.tutorial4.session1 1.needforsharedsemanticsMustafa Jarrar
 
CSTalks-Natural Language Processing-17Aug
CSTalks-Natural Language Processing-17AugCSTalks-Natural Language Processing-17Aug
CSTalks-Natural Language Processing-17Augcstalks
 
Python training in delhi, request demo class (2)
Python training in delhi, request demo class (2)Python training in delhi, request demo class (2)
Python training in delhi, request demo class (2)vikasAT
 
Python training in delhi, request demo class
Python training in delhi, request demo classPython training in delhi, request demo class
Python training in delhi, request demo classvikasAT
 
Pal gov.tutorial2.session0.outline
Pal gov.tutorial2.session0.outlinePal gov.tutorial2.session0.outline
Pal gov.tutorial2.session0.outlineMustafa Jarrar
 
Python training in delhi, request demo class (1)
Python training in delhi, request demo class (1)Python training in delhi, request demo class (1)
Python training in delhi, request demo class (1)vikasAT
 
Pal gov.tutorial3.session12.lab5
Pal gov.tutorial3.session12.lab5Pal gov.tutorial3.session12.lab5
Pal gov.tutorial3.session12.lab5Mustafa Jarrar
 
Overview of the MediaEval 2012 Tagging Task
Overview of the MediaEval 2012 Tagging TaskOverview of the MediaEval 2012 Tagging Task
Overview of the MediaEval 2012 Tagging TaskMediaEval2012
 
Open-source machine translation for Icelandic: the Apertium platform as an o...
Open-source machine translation for Icelandic:
 the Apertium platform as an o...Open-source machine translation for Icelandic:
 the Apertium platform as an o...
Open-source machine translation for Icelandic: the Apertium platform as an o...Forcada Mikel
 
Continual Reinforcement Learning in 3D Non-stationary Environments
Continual Reinforcement Learning in 3D Non-stationary EnvironmentsContinual Reinforcement Learning in 3D Non-stationary Environments
Continual Reinforcement Learning in 3D Non-stationary EnvironmentsVincenzo Lomonaco
 
Pal gov.tutorial2.session5 1.rdf_jarrar
Pal gov.tutorial2.session5 1.rdf_jarrarPal gov.tutorial2.session5 1.rdf_jarrar
Pal gov.tutorial2.session5 1.rdf_jarrarMustafa Jarrar
 
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
 
Using Virtual Reality for Interpreter-mediated Communication and Training
Using Virtual Reality for Interpreter-mediated Communication and TrainingUsing Virtual Reality for Interpreter-mediated Communication and Training
Using Virtual Reality for Interpreter-mediated Communication and TrainingPanagiotis Ritsos
 

Mais procurados (19)

Pal gov.tutorial4.session13.arabicontology
Pal gov.tutorial4.session13.arabicontologyPal gov.tutorial4.session13.arabicontology
Pal gov.tutorial4.session13.arabicontology
 
Pal gov.tutorial4.session11.lab zinnarontologybasedwebservices
Pal gov.tutorial4.session11.lab zinnarontologybasedwebservicesPal gov.tutorial4.session11.lab zinnarontologybasedwebservices
Pal gov.tutorial4.session11.lab zinnarontologybasedwebservices
 
Pal gov.tutorial4.session7.lab legalpersonontology
Pal gov.tutorial4.session7.lab legalpersonontologyPal gov.tutorial4.session7.lab legalpersonontology
Pal gov.tutorial4.session7.lab legalpersonontology
 
Pal gov.tutorial4.outline
Pal gov.tutorial4.outlinePal gov.tutorial4.outline
Pal gov.tutorial4.outline
 
Pal gov.tutorial4.session6 2.knowledge double-articulation
Pal gov.tutorial4.session6 2.knowledge double-articulationPal gov.tutorial4.session6 2.knowledge double-articulation
Pal gov.tutorial4.session6 2.knowledge double-articulation
 
Pal gov.tutorial4.session1 1.needforsharedsemantics
Pal gov.tutorial4.session1 1.needforsharedsemanticsPal gov.tutorial4.session1 1.needforsharedsemantics
Pal gov.tutorial4.session1 1.needforsharedsemantics
 
CSTalks-Natural Language Processing-17Aug
CSTalks-Natural Language Processing-17AugCSTalks-Natural Language Processing-17Aug
CSTalks-Natural Language Processing-17Aug
 
Python training in delhi, request demo class (2)
Python training in delhi, request demo class (2)Python training in delhi, request demo class (2)
Python training in delhi, request demo class (2)
 
Python training in delhi, request demo class
Python training in delhi, request demo classPython training in delhi, request demo class
Python training in delhi, request demo class
 
Pal gov.tutorial2.session0.outline
Pal gov.tutorial2.session0.outlinePal gov.tutorial2.session0.outline
Pal gov.tutorial2.session0.outline
 
Python training in delhi, request demo class (1)
Python training in delhi, request demo class (1)Python training in delhi, request demo class (1)
Python training in delhi, request demo class (1)
 
Pal gov.tutorial3.session12.lab5
Pal gov.tutorial3.session12.lab5Pal gov.tutorial3.session12.lab5
Pal gov.tutorial3.session12.lab5
 
Overview of the MediaEval 2012 Tagging Task
Overview of the MediaEval 2012 Tagging TaskOverview of the MediaEval 2012 Tagging Task
Overview of the MediaEval 2012 Tagging Task
 
Open-source machine translation for Icelandic: the Apertium platform as an o...
Open-source machine translation for Icelandic:
 the Apertium platform as an o...Open-source machine translation for Icelandic:
 the Apertium platform as an o...
Open-source machine translation for Icelandic: the Apertium platform as an o...
 
Continual Reinforcement Learning in 3D Non-stationary Environments
Continual Reinforcement Learning in 3D Non-stationary EnvironmentsContinual Reinforcement Learning in 3D Non-stationary Environments
Continual Reinforcement Learning in 3D Non-stationary Environments
 
Pal gov.tutorial2.session5 1.rdf_jarrar
Pal gov.tutorial2.session5 1.rdf_jarrarPal gov.tutorial2.session5 1.rdf_jarrar
Pal gov.tutorial2.session5 1.rdf_jarrar
 
Callii2012 b.docx
Callii2012 b.docxCallii2012 b.docx
Callii2012 b.docx
 
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)
 
Using Virtual Reality for Interpreter-mediated Communication and Training
Using Virtual Reality for Interpreter-mediated Communication and TrainingUsing Virtual Reality for Interpreter-mediated Communication and Training
Using Virtual Reality for Interpreter-mediated Communication and Training
 

Semelhante a Pal gov.tutorial4.session6 1.ontologyengineeringchallenges

Pal gov.tutorial4.session12 1.lexicalsemanitcs
Pal gov.tutorial4.session12 1.lexicalsemanitcsPal gov.tutorial4.session12 1.lexicalsemanitcs
Pal gov.tutorial4.session12 1.lexicalsemanitcsMustafa Jarrar
 
Pal gov.tutorial4.session1 1.needforsharedsemantics
Pal gov.tutorial4.session1 1.needforsharedsemanticsPal gov.tutorial4.session1 1.needforsharedsemantics
Pal gov.tutorial4.session1 1.needforsharedsemanticsMustafa Jarrar
 
Jarrar: Ontology Engineering and Double Articulation
Jarrar: Ontology Engineering and Double ArticulationJarrar: Ontology Engineering and Double Articulation
Jarrar: Ontology Engineering and Double ArticulationMustafa Jarrar
 
Pal gov.tutorial2.session7
Pal gov.tutorial2.session7Pal gov.tutorial2.session7
Pal gov.tutorial2.session7Mustafa Jarrar
 
Pal gov.tutorial2.session7.owl
Pal gov.tutorial2.session7.owlPal gov.tutorial2.session7.owl
Pal gov.tutorial2.session7.owlMustafa Jarrar
 
Pal gov.tutorial3.session5.lab2
Pal gov.tutorial3.session5.lab2Pal gov.tutorial3.session5.lab2
Pal gov.tutorial3.session5.lab2Mustafa Jarrar
 
Pal gov.tutorial3.session0.outline
Pal gov.tutorial3.session0.outlinePal gov.tutorial3.session0.outline
Pal gov.tutorial3.session0.outlineMustafa Jarrar
 
Pal gov.tutorial3.session14.lab6
Pal gov.tutorial3.session14.lab6Pal gov.tutorial3.session14.lab6
Pal gov.tutorial3.session14.lab6Mustafa Jarrar
 
Pal gov.tutorial2.session8.lab owl
Pal gov.tutorial2.session8.lab owlPal gov.tutorial2.session8.lab owl
Pal gov.tutorial2.session8.lab owlMustafa Jarrar
 
Pal gov.tutorial2.session12 1.the problem of data integration
Pal gov.tutorial2.session12 1.the problem of data integrationPal gov.tutorial2.session12 1.the problem of data integration
Pal gov.tutorial2.session12 1.the problem of data integrationMustafa Jarrar
 
Pal gov.tutorial2.session1.xml basics and namespaces
Pal gov.tutorial2.session1.xml basics and namespacesPal gov.tutorial2.session1.xml basics and namespaces
Pal gov.tutorial2.session1.xml basics and namespacesMustafa Jarrar
 
Pal gov.tutorial1.session1 3.conceptualschemadesignsteps
Pal gov.tutorial1.session1 3.conceptualschemadesignstepsPal gov.tutorial1.session1 3.conceptualschemadesignsteps
Pal gov.tutorial1.session1 3.conceptualschemadesignstepsMustafa Jarrar
 
Pal gov.tutorial1.session1 1.informationmodeling
Pal gov.tutorial1.session1 1.informationmodelingPal gov.tutorial1.session1 1.informationmodeling
Pal gov.tutorial1.session1 1.informationmodelingMustafa Jarrar
 
Pal gov.tutorial2.session15 1.linkeddata
Pal gov.tutorial2.session15 1.linkeddataPal gov.tutorial2.session15 1.linkeddata
Pal gov.tutorial2.session15 1.linkeddataMustafa Jarrar
 
Pal gov.tutorial2.session4.lab xml document and schemas
Pal gov.tutorial2.session4.lab xml  document and schemasPal gov.tutorial2.session4.lab xml  document and schemas
Pal gov.tutorial2.session4.lab xml document and schemasMustafa Jarrar
 
Question answer template
Question answer templateQuestion answer template
Question answer templateThanuw Chaks
 
Pal gov.tutorial1.session3 1.uniquenessrules
Pal gov.tutorial1.session3 1.uniquenessrulesPal gov.tutorial1.session3 1.uniquenessrules
Pal gov.tutorial1.session3 1.uniquenessrulesMustafa Jarrar
 
Pal gov.tutorial2.session2.xml dtd's
Pal gov.tutorial2.session2.xml dtd'sPal gov.tutorial2.session2.xml dtd's
Pal gov.tutorial2.session2.xml dtd'sMustafa Jarrar
 
Pal gov.tutorial2.session16.lab rd-fa
Pal gov.tutorial2.session16.lab rd-faPal gov.tutorial2.session16.lab rd-fa
Pal gov.tutorial2.session16.lab rd-faMustafa Jarrar
 
Pal gov.tutorial2.session13 1.data schema integration
Pal gov.tutorial2.session13 1.data schema integrationPal gov.tutorial2.session13 1.data schema integration
Pal gov.tutorial2.session13 1.data schema integrationMustafa Jarrar
 

Semelhante a Pal gov.tutorial4.session6 1.ontologyengineeringchallenges (20)

Pal gov.tutorial4.session12 1.lexicalsemanitcs
Pal gov.tutorial4.session12 1.lexicalsemanitcsPal gov.tutorial4.session12 1.lexicalsemanitcs
Pal gov.tutorial4.session12 1.lexicalsemanitcs
 
Pal gov.tutorial4.session1 1.needforsharedsemantics
Pal gov.tutorial4.session1 1.needforsharedsemanticsPal gov.tutorial4.session1 1.needforsharedsemantics
Pal gov.tutorial4.session1 1.needforsharedsemantics
 
Jarrar: Ontology Engineering and Double Articulation
Jarrar: Ontology Engineering and Double ArticulationJarrar: Ontology Engineering and Double Articulation
Jarrar: Ontology Engineering and Double Articulation
 
Pal gov.tutorial2.session7
Pal gov.tutorial2.session7Pal gov.tutorial2.session7
Pal gov.tutorial2.session7
 
Pal gov.tutorial2.session7.owl
Pal gov.tutorial2.session7.owlPal gov.tutorial2.session7.owl
Pal gov.tutorial2.session7.owl
 
Pal gov.tutorial3.session5.lab2
Pal gov.tutorial3.session5.lab2Pal gov.tutorial3.session5.lab2
Pal gov.tutorial3.session5.lab2
 
Pal gov.tutorial3.session0.outline
Pal gov.tutorial3.session0.outlinePal gov.tutorial3.session0.outline
Pal gov.tutorial3.session0.outline
 
Pal gov.tutorial3.session14.lab6
Pal gov.tutorial3.session14.lab6Pal gov.tutorial3.session14.lab6
Pal gov.tutorial3.session14.lab6
 
Pal gov.tutorial2.session8.lab owl
Pal gov.tutorial2.session8.lab owlPal gov.tutorial2.session8.lab owl
Pal gov.tutorial2.session8.lab owl
 
Pal gov.tutorial2.session12 1.the problem of data integration
Pal gov.tutorial2.session12 1.the problem of data integrationPal gov.tutorial2.session12 1.the problem of data integration
Pal gov.tutorial2.session12 1.the problem of data integration
 
Pal gov.tutorial2.session1.xml basics and namespaces
Pal gov.tutorial2.session1.xml basics and namespacesPal gov.tutorial2.session1.xml basics and namespaces
Pal gov.tutorial2.session1.xml basics and namespaces
 
Pal gov.tutorial1.session1 3.conceptualschemadesignsteps
Pal gov.tutorial1.session1 3.conceptualschemadesignstepsPal gov.tutorial1.session1 3.conceptualschemadesignsteps
Pal gov.tutorial1.session1 3.conceptualschemadesignsteps
 
Pal gov.tutorial1.session1 1.informationmodeling
Pal gov.tutorial1.session1 1.informationmodelingPal gov.tutorial1.session1 1.informationmodeling
Pal gov.tutorial1.session1 1.informationmodeling
 
Pal gov.tutorial2.session15 1.linkeddata
Pal gov.tutorial2.session15 1.linkeddataPal gov.tutorial2.session15 1.linkeddata
Pal gov.tutorial2.session15 1.linkeddata
 
Pal gov.tutorial2.session4.lab xml document and schemas
Pal gov.tutorial2.session4.lab xml  document and schemasPal gov.tutorial2.session4.lab xml  document and schemas
Pal gov.tutorial2.session4.lab xml document and schemas
 
Question answer template
Question answer templateQuestion answer template
Question answer template
 
Pal gov.tutorial1.session3 1.uniquenessrules
Pal gov.tutorial1.session3 1.uniquenessrulesPal gov.tutorial1.session3 1.uniquenessrules
Pal gov.tutorial1.session3 1.uniquenessrules
 
Pal gov.tutorial2.session2.xml dtd's
Pal gov.tutorial2.session2.xml dtd'sPal gov.tutorial2.session2.xml dtd's
Pal gov.tutorial2.session2.xml dtd's
 
Pal gov.tutorial2.session16.lab rd-fa
Pal gov.tutorial2.session16.lab rd-faPal gov.tutorial2.session16.lab rd-fa
Pal gov.tutorial2.session16.lab rd-fa
 
Pal gov.tutorial2.session13 1.data schema integration
Pal gov.tutorial2.session13 1.data schema integrationPal gov.tutorial2.session13 1.data schema integration
Pal gov.tutorial2.session13 1.data schema integration
 

Mais de Mustafa Jarrar

Clustering Arabic Tweets for Sentiment Analysis
Clustering Arabic Tweets for Sentiment AnalysisClustering Arabic Tweets for Sentiment Analysis
Clustering Arabic Tweets for Sentiment AnalysisMustafa Jarrar
 
Classifying Processes and Basic Formal Ontology
Classifying Processes  and Basic Formal OntologyClassifying Processes  and Basic Formal Ontology
Classifying Processes and Basic Formal OntologyMustafa Jarrar
 
Discrete Mathematics Course Outline
Discrete Mathematics Course OutlineDiscrete Mathematics Course Outline
Discrete Mathematics Course OutlineMustafa Jarrar
 
Business Process Implementation
Business Process ImplementationBusiness Process Implementation
Business Process ImplementationMustafa Jarrar
 
Business Process Design and Re-engineering
Business Process Design and Re-engineeringBusiness Process Design and Re-engineering
Business Process Design and Re-engineeringMustafa Jarrar
 
BPMN 2.0 Analytical Constructs
BPMN 2.0 Analytical ConstructsBPMN 2.0 Analytical Constructs
BPMN 2.0 Analytical ConstructsMustafa Jarrar
 
BPMN 2.0 Descriptive Constructs
BPMN 2.0 Descriptive Constructs  BPMN 2.0 Descriptive Constructs
BPMN 2.0 Descriptive Constructs Mustafa Jarrar
 
Introduction to Business Process Management
Introduction to Business Process ManagementIntroduction to Business Process Management
Introduction to Business Process ManagementMustafa Jarrar
 
Customer Complaint Ontology
Customer Complaint Ontology Customer Complaint Ontology
Customer Complaint Ontology Mustafa Jarrar
 
Subset, Equality, and Exclusion Rules
Subset, Equality, and Exclusion RulesSubset, Equality, and Exclusion Rules
Subset, Equality, and Exclusion RulesMustafa Jarrar
 
Schema Modularization in ORM
Schema Modularization in ORMSchema Modularization in ORM
Schema Modularization in ORMMustafa Jarrar
 
On Computer Science Trends and Priorities in Palestine
On Computer Science Trends and Priorities in PalestineOn Computer Science Trends and Priorities in Palestine
On Computer Science Trends and Priorities in PalestineMustafa Jarrar
 
Lessons from Class Recording & Publishing of Eight Online Courses
Lessons from Class Recording & Publishing of Eight Online CoursesLessons from Class Recording & Publishing of Eight Online Courses
Lessons from Class Recording & Publishing of Eight Online CoursesMustafa Jarrar
 
Presentation curras paper-emnlp2014-final
Presentation curras paper-emnlp2014-finalPresentation curras paper-emnlp2014-final
Presentation curras paper-emnlp2014-finalMustafa Jarrar
 
Jarrar: Future Internet in Horizon 2020 Calls
Jarrar: Future Internet in Horizon 2020 CallsJarrar: Future Internet in Horizon 2020 Calls
Jarrar: Future Internet in Horizon 2020 CallsMustafa Jarrar
 
Habash: Arabic Natural Language Processing
Habash: Arabic Natural Language ProcessingHabash: Arabic Natural Language Processing
Habash: Arabic Natural Language ProcessingMustafa Jarrar
 
Adnan: Introduction to Natural Language Processing
Adnan: Introduction to Natural Language Processing Adnan: Introduction to Natural Language Processing
Adnan: Introduction to Natural Language Processing Mustafa Jarrar
 
Riestra: How to Design and engineer Competitive Horizon 2020 Proposals
Riestra: How to Design and engineer Competitive Horizon 2020 ProposalsRiestra: How to Design and engineer Competitive Horizon 2020 Proposals
Riestra: How to Design and engineer Competitive Horizon 2020 ProposalsMustafa Jarrar
 
Bouquet: SIERA Workshop on The Pillars of Horizon2020
Bouquet: SIERA Workshop on The Pillars of Horizon2020Bouquet: SIERA Workshop on The Pillars of Horizon2020
Bouquet: SIERA Workshop on The Pillars of Horizon2020Mustafa Jarrar
 
Jarrar: Sparql Project
Jarrar: Sparql ProjectJarrar: Sparql Project
Jarrar: Sparql ProjectMustafa Jarrar
 

Mais de Mustafa Jarrar (20)

Clustering Arabic Tweets for Sentiment Analysis
Clustering Arabic Tweets for Sentiment AnalysisClustering Arabic Tweets for Sentiment Analysis
Clustering Arabic Tweets for Sentiment Analysis
 
Classifying Processes and Basic Formal Ontology
Classifying Processes  and Basic Formal OntologyClassifying Processes  and Basic Formal Ontology
Classifying Processes and Basic Formal Ontology
 
Discrete Mathematics Course Outline
Discrete Mathematics Course OutlineDiscrete Mathematics Course Outline
Discrete Mathematics Course Outline
 
Business Process Implementation
Business Process ImplementationBusiness Process Implementation
Business Process Implementation
 
Business Process Design and Re-engineering
Business Process Design and Re-engineeringBusiness Process Design and Re-engineering
Business Process Design and Re-engineering
 
BPMN 2.0 Analytical Constructs
BPMN 2.0 Analytical ConstructsBPMN 2.0 Analytical Constructs
BPMN 2.0 Analytical Constructs
 
BPMN 2.0 Descriptive Constructs
BPMN 2.0 Descriptive Constructs  BPMN 2.0 Descriptive Constructs
BPMN 2.0 Descriptive Constructs
 
Introduction to Business Process Management
Introduction to Business Process ManagementIntroduction to Business Process Management
Introduction to Business Process Management
 
Customer Complaint Ontology
Customer Complaint Ontology Customer Complaint Ontology
Customer Complaint Ontology
 
Subset, Equality, and Exclusion Rules
Subset, Equality, and Exclusion RulesSubset, Equality, and Exclusion Rules
Subset, Equality, and Exclusion Rules
 
Schema Modularization in ORM
Schema Modularization in ORMSchema Modularization in ORM
Schema Modularization in ORM
 
On Computer Science Trends and Priorities in Palestine
On Computer Science Trends and Priorities in PalestineOn Computer Science Trends and Priorities in Palestine
On Computer Science Trends and Priorities in Palestine
 
Lessons from Class Recording & Publishing of Eight Online Courses
Lessons from Class Recording & Publishing of Eight Online CoursesLessons from Class Recording & Publishing of Eight Online Courses
Lessons from Class Recording & Publishing of Eight Online Courses
 
Presentation curras paper-emnlp2014-final
Presentation curras paper-emnlp2014-finalPresentation curras paper-emnlp2014-final
Presentation curras paper-emnlp2014-final
 
Jarrar: Future Internet in Horizon 2020 Calls
Jarrar: Future Internet in Horizon 2020 CallsJarrar: Future Internet in Horizon 2020 Calls
Jarrar: Future Internet in Horizon 2020 Calls
 
Habash: Arabic Natural Language Processing
Habash: Arabic Natural Language ProcessingHabash: Arabic Natural Language Processing
Habash: Arabic Natural Language Processing
 
Adnan: Introduction to Natural Language Processing
Adnan: Introduction to Natural Language Processing Adnan: Introduction to Natural Language Processing
Adnan: Introduction to Natural Language Processing
 
Riestra: How to Design and engineer Competitive Horizon 2020 Proposals
Riestra: How to Design and engineer Competitive Horizon 2020 ProposalsRiestra: How to Design and engineer Competitive Horizon 2020 Proposals
Riestra: How to Design and engineer Competitive Horizon 2020 Proposals
 
Bouquet: SIERA Workshop on The Pillars of Horizon2020
Bouquet: SIERA Workshop on The Pillars of Horizon2020Bouquet: SIERA Workshop on The Pillars of Horizon2020
Bouquet: SIERA Workshop on The Pillars of Horizon2020
 
Jarrar: Sparql Project
Jarrar: Sparql ProjectJarrar: Sparql Project
Jarrar: Sparql Project
 

Último

The basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptxThe basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptxheathfieldcps1
 
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptxMaritesTamaniVerdade
 
Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...
Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...
Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...Shubhangi Sonawane
 
Seal of Good Local Governance (SGLG) 2024Final.pptx
Seal of Good Local Governance (SGLG) 2024Final.pptxSeal of Good Local Governance (SGLG) 2024Final.pptx
Seal of Good Local Governance (SGLG) 2024Final.pptxnegromaestrong
 
Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfActivity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfciinovamais
 
1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi  6.pdf1029-Danh muc Sach Giao Khoa khoi  6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdfQucHHunhnh
 
Key note speaker Neum_Admir Softic_ENG.pdf
Key note speaker Neum_Admir Softic_ENG.pdfKey note speaker Neum_Admir Softic_ENG.pdf
Key note speaker Neum_Admir Softic_ENG.pdfAdmir Softic
 
Sociology 101 Demonstration of Learning Exhibit
Sociology 101 Demonstration of Learning ExhibitSociology 101 Demonstration of Learning Exhibit
Sociology 101 Demonstration of Learning Exhibitjbellavia9
 
The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxheathfieldcps1
 
Z Score,T Score, Percential Rank and Box Plot Graph
Z Score,T Score, Percential Rank and Box Plot GraphZ Score,T Score, Percential Rank and Box Plot Graph
Z Score,T Score, Percential Rank and Box Plot GraphThiyagu K
 
Introduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The BasicsIntroduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The BasicsTechSoup
 
microwave assisted reaction. General introduction
microwave assisted reaction. General introductionmicrowave assisted reaction. General introduction
microwave assisted reaction. General introductionMaksud Ahmed
 
Unit-IV; Professional Sales Representative (PSR).pptx
Unit-IV; Professional Sales Representative (PSR).pptxUnit-IV; Professional Sales Representative (PSR).pptx
Unit-IV; Professional Sales Representative (PSR).pptxVishalSingh1417
 
ComPTIA Overview | Comptia Security+ Book SY0-701
ComPTIA Overview | Comptia Security+ Book SY0-701ComPTIA Overview | Comptia Security+ Book SY0-701
ComPTIA Overview | Comptia Security+ Book SY0-701bronxfugly43
 
Role Of Transgenic Animal In Target Validation-1.pptx
Role Of Transgenic Animal In Target Validation-1.pptxRole Of Transgenic Animal In Target Validation-1.pptx
Role Of Transgenic Animal In Target Validation-1.pptxNikitaBankoti2
 
Food Chain and Food Web (Ecosystem) EVS, B. Pharmacy 1st Year, Sem-II
Food Chain and Food Web (Ecosystem) EVS, B. Pharmacy 1st Year, Sem-IIFood Chain and Food Web (Ecosystem) EVS, B. Pharmacy 1st Year, Sem-II
Food Chain and Food Web (Ecosystem) EVS, B. Pharmacy 1st Year, Sem-IIShubhangi Sonawane
 
Grant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingGrant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingTechSoup
 
Class 11th Physics NEET formula sheet pdf
Class 11th Physics NEET formula sheet pdfClass 11th Physics NEET formula sheet pdf
Class 11th Physics NEET formula sheet pdfAyushMahapatra5
 

Último (20)

The basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptxThe basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptx
 
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
 
Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...
Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...
Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...
 
Seal of Good Local Governance (SGLG) 2024Final.pptx
Seal of Good Local Governance (SGLG) 2024Final.pptxSeal of Good Local Governance (SGLG) 2024Final.pptx
Seal of Good Local Governance (SGLG) 2024Final.pptx
 
Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfActivity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdf
 
1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi  6.pdf1029-Danh muc Sach Giao Khoa khoi  6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdf
 
Mehran University Newsletter Vol-X, Issue-I, 2024
Mehran University Newsletter Vol-X, Issue-I, 2024Mehran University Newsletter Vol-X, Issue-I, 2024
Mehran University Newsletter Vol-X, Issue-I, 2024
 
Key note speaker Neum_Admir Softic_ENG.pdf
Key note speaker Neum_Admir Softic_ENG.pdfKey note speaker Neum_Admir Softic_ENG.pdf
Key note speaker Neum_Admir Softic_ENG.pdf
 
Sociology 101 Demonstration of Learning Exhibit
Sociology 101 Demonstration of Learning ExhibitSociology 101 Demonstration of Learning Exhibit
Sociology 101 Demonstration of Learning Exhibit
 
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptxINDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
 
The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptx
 
Z Score,T Score, Percential Rank and Box Plot Graph
Z Score,T Score, Percential Rank and Box Plot GraphZ Score,T Score, Percential Rank and Box Plot Graph
Z Score,T Score, Percential Rank and Box Plot Graph
 
Introduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The BasicsIntroduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The Basics
 
microwave assisted reaction. General introduction
microwave assisted reaction. General introductionmicrowave assisted reaction. General introduction
microwave assisted reaction. General introduction
 
Unit-IV; Professional Sales Representative (PSR).pptx
Unit-IV; Professional Sales Representative (PSR).pptxUnit-IV; Professional Sales Representative (PSR).pptx
Unit-IV; Professional Sales Representative (PSR).pptx
 
ComPTIA Overview | Comptia Security+ Book SY0-701
ComPTIA Overview | Comptia Security+ Book SY0-701ComPTIA Overview | Comptia Security+ Book SY0-701
ComPTIA Overview | Comptia Security+ Book SY0-701
 
Role Of Transgenic Animal In Target Validation-1.pptx
Role Of Transgenic Animal In Target Validation-1.pptxRole Of Transgenic Animal In Target Validation-1.pptx
Role Of Transgenic Animal In Target Validation-1.pptx
 
Food Chain and Food Web (Ecosystem) EVS, B. Pharmacy 1st Year, Sem-II
Food Chain and Food Web (Ecosystem) EVS, B. Pharmacy 1st Year, Sem-IIFood Chain and Food Web (Ecosystem) EVS, B. Pharmacy 1st Year, Sem-II
Food Chain and Food Web (Ecosystem) EVS, B. Pharmacy 1st Year, Sem-II
 
Grant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingGrant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy Consulting
 
Class 11th Physics NEET formula sheet pdf
Class 11th Physics NEET formula sheet pdfClass 11th Physics NEET formula sheet pdf
Class 11th Physics NEET formula sheet pdf
 

Pal gov.tutorial4.session6 1.ontologyengineeringchallenges

  • 1. ‫أكاديمية الحكومة اإللكترونية الفلسطينية‬ The Palestinian eGovernment Academy www.egovacademy.ps Tutorial 4: Ontology Engineering & Lexical Semantics Session 6.1 Ontology Engineering Challenges Dr. Mustafa Jarrar University of Birzeit mjarrar@birzeit.edu www.jarrar.info PalGov © 2011 1
  • 2. About This tutorial is part of the PalGov project, funded by the TEMPUS IV program of the Commission of the European Communities, grant agreement 511159-TEMPUS-1- 2010-1-PS-TEMPUS-JPHES. The project website: www.egovacademy.ps Project Consortium: Birzeit University, Palestine University of Trento, Italy (Coordinator ) Palestine Polytechnic University, Palestine Vrije Universiteit Brussel, Belgium Palestine Technical University, Palestine Université de Savoie, France Ministry of Telecom and IT, Palestine University of Namur, Belgium Ministry of Interior, Palestine TrueTrust, UK Ministry of Local Government, Palestine Coordinator: Dr. Mustafa Jarrar Birzeit University, P.O.Box 14- Birzeit, Palestine Telfax:+972 2 2982935 mjarrar@birzeit.eduPalGov © 2011 2
  • 3. © Copyright Notes Everyone is encouraged to use this material, or part of it, but should properly cite the project (logo and website), and the author of that part. No part of this tutorial may be reproduced or modified in any form or by any means, without prior written permission from the project, who have the full copyrights on the material. Attribution-NonCommercial-ShareAlike CC-BY-NC-SA This license lets others remix, tweak, and build upon your work non- commercially, as long as they credit you and license their new creations under the identical terms. PalGov © 2011 3
  • 4. Tutorial Map Topic Time Session 1_1: The Need for Sharing Semantics 1.5 Session 1_2: What is an ontology 1.5 Intended Learning Objectives A: Knowledge and Understanding Session 2: Lab- Build a Population Ontology 3 4a1: Demonstrate knowledge of what is an ontology, Session 3: Lab- Build a BankCustomer Ontology 3 how it is built, and what it is used for. Session 4: Lab- Build a BankCustomer Ontology 3 4a2: Demonstrate knowledge of ontology engineering and evaluation. Session 5: Lab- Ontology Tools 3 4a3: Describe the difference between an ontology and a Session 6_1: Ontology Engineering Challenges 1.5 schema, and an ontology and a dictionary. Session 6_2: Ontology Double Articulation 1.5 4a4: Explain the concept of language ontologies, lexical semantics and multilingualism. Session 7: Lab - Build a Legal-Person Ontology 3 B: Intellectual Skills Session 8_1: Ontology Modeling Challenges 1.5 4b1: Develop quality ontologies. Session 8_2: Stepwise Methodologies 1.5 4b2: Tackle ontology engineering challenges. 4b3: Develop multilingual ontologies. Session 9: Lab - Build a Legal-Person Ontology 3 4b4: Formulate quality glosses. Session 10: Zinnar – The Palestinian eGovernment 3 C: Professional and Practical Skills Interoperability Framework 4c1: Use ontology tools. Session 11: Lab- Using Zinnar in web services 3 4c2: (Re)use existing Language ontologies. Session 12_1: Lexical Semantics and Multilingually 1.5 D: General and Transferable Skills d1: Working with team. Session 12_2: WordNets 1.5 d2: Presenting and defending ideas. Session 13: ArabicOntology 3 d3: Use of creativity and innovation in problem solving. Session 14: Lab-Using Linguistic Ontologies 3 d4: Develop communication skills and logical reasoning abilities. Session 15: Lab-Using Linguistic Ontologies 3 PalGov © 2011 4
  • 5. Outline and Session ILOs This session will help students to: 4a2: Demonstrate knowledge of ontology engineering and evaluation. 4b2: Tackle ontology engineering challenges. PalGov © 2011 5
  • 6. Ontology Engineering Challenges  Ontology Usability versus Ontology Reusability  Ontology Application Dependence • Only these challenges will be discussed, but there are many other challenges that may face an ontology engineer. • Discussing such challenges will help improve the modeling skills of an ontology engineer. PalGov © 2011 6
  • 7. Ontology Reusability vs Usability Given 4 different LegalPerson ontologies (which is more usable/reusable?) O1  Used by App1, 9000 times/day.  Used by App1, 1000 times/day. O2  Used by App2, 1000 times/day.  Used by App1, 100 times/day. O3  Used by App2, 100 times/day.  Used by App3, 100 times/day.  Used by App1, 10 times/day. O4  Used by App2, 10 times/day.  Used by App3, 10 times/day.  Used by App4, 10000 times/day. App1: Ministries’ Web Service to exchange companies’ profiles is based on this ontology. App2: Champers of commerce’s Web Service to exchange companies’ profiles, based on this ontology. App3: Banks designed their “new account” form, based on the company properties in this ontology (off time use). App4: Lawyers refer to the definition of “company”, as stated in this ontology (off time use). PalGov © 2011 7
  • 8. Ontology Reusability vs Usability Given 4 different LegalPerson ontologies (which is more usable/reusable?) O1  Used of different times/day. Usability: maximizing the numberby App1, 9000 applications using an ontology for the same kind of task.  Used by App1, 1000 times/day. Reusability: maximizing the number of different applications using O2  Used by App2, 1000 times/day. an ontology over different kind of tasks. Why Reusability:  Used by App1, 100 times/day. 1) Saving time, cost, and efforts… O3  Used by App2, 100 times/day. 2) Increasing reliability: the more reused the more tested. 3) An important quality  Used by App3, 100 times/day. factor: a highly reusable ontology is an indication that it is a good ontology.  Used by App1, 10 times/day. How to increase Usability? Used by App2, 10 times/day.  O4 by being closer to the application specifications and requirements at hand.  Used by App3, 10 times/day.  How to increase Reusability?Used by App4, 10 times/day. App1: Ministries Web Service to exchange companies profiles is based on this ontology. i.e. be more by taking into account different usages/applications, App2: Champers of commerce’s Web Service to exchange companies profiles, based on this ontology. general. App3: Banks designed their “new account” form, based on the company properties in this ontology (off time use). App4: Lawyers refer to the definition of “company”, as stated in this ontology (off time use). PalGov © 2011 8
  • 9. Ontology Reusability vs Usability Given 4 different LegalPerson ontologies (which is more usable/reusable?) O1  Used of different times/day. Usability: maximizing the numberby App1, 9000 applications using an ontology for the same kind of task.  Used by App1, 1000 times/day. Reusability: maximizing the number of different applications using O2 an ontology over Reusability Used by App2, 1000 times/day.  Usability different kind of tasks. Why Reusability: usability and reusability 100 times/day. Tradeoff between  Used by App1, 1) Savings in time, cost, and efforts… O3  Used by App2, 100 times/day. 2) The more an ontology is more reusedless more tested. be, Increasing reliability: the usable the the reusable it will 3) and important quality  Used by App3, 100 times/day. An vice versa. factor: a highly resalable ontology is an  A good ontologyis a good ontology. indication that it engineer knowsApp1, 10 times/day.  Used by how/where to compromise this tradeoff. How to increase Usability? Used by App2, 10 times/day.  O4 by being closes to the application specifics and requirements at hand.  Used by App3, 10 times/day.  How to increase Reusability?Used by App4, 10 times/day. App1: Ministries Web Service to exchange companies profiles is based on this ontology. i.e. be more by taking into account different usages/applications, App2: Champers of commerce’s Web Service to exchange companies profiles, based on this ontology. general. App3: Banks designed their “new account” form, based on the company properties in this ontology (off time use). App4: Lawyers refer to the definition of “company”, as stated in this ontology (off time use). PalGov © 2011 9
  • 10. Ontology Application Dependence Ontologies are supposed to capture knowledge at the domain level independently of application requirements [G97] [GB99] [CJB99]. The problem is that when building an ontology, there will always be intended or expected usability requirements -“at hand”- which influence the independency level of ontology axioms. This problem is as the Interaction Problem: “Representing knowledge for the purpose of solving some problem is strongly affected by the nature of the problem and the inference strategy to be applied to the problem.” Bylander and Chandrasekaran in [BC88] PalGov © 2011 10
  • 11. Ontology Application Dependence What is the meaning of a “book” here? Applications Bookstores ? Applications Library  Usability perspectives lead to different (and sometimes conflicting) axiomatizations although these axiomatizations might agree at the domain level. PalGov © 2011 11
  • 12. Ontology Application Dependence What is the meaning of a “book” here? Applications Bookstores Both are not ontologies, they are data schemes. ? Can you build a useful and an application-independent ontology? Applications Library  Usability perspectives lead to different (and sometimes conflicting) axiomatizations although these axiomatizations might agree at the domain level. PalGov © 2011 12
  • 13. References Mustafa Jarrar: Towards methodological principles for ontology engineering. PhD Thesis. Vrije Universiteit Brussel. (May 2005) Thomas R. Gruber: Toward Principles for the Design of Ontologies Used for Knowledge Sharing http://tomgruber.org/writing/onto-design.pdf Nicola Guarino: Formal Ontology and Information Systems http://www.loa- cnr.it/Papers/FOIS98.pdf Guarino, N.: Understanding, building, and using ontologies: A commentary to “Using Explicit Ontologies in KBS Development”, by van Heijst, Schreiber, and Wielinga." International Journal of Human and Computer Studies No. 46. (1997) pp. 293–310 [HV93] Hemmann, T., Voss, H.: A Reusable and Specializable Interpretation Model for ModelBased Diagnosis. In: Luckenhoff, C., Fensel, D., Studer, D. (eds.): Proceeding 3rd KADS Meeting Siemens AG. Munich. March (1993) pp. 189–205 PalGov © 2011 13