Reusing ontologies in Competence Development programmes: RuleML and its capabilitie

1. SU “St. Kl. Ohridksi”, FMI, IT Reusing ontologies in Competence Developmentprogrammes: RuleML and its capabilitie Kornelia Todorova, Krassen Stefanov, University of Sofia “St. Kliment Ohridski”, cornelia@fmi.uni-sofia.bg.krassen@fmi.uni-sofia.bg TENCompetence, Sofia, Bulgaria

2. SU “St. Kl. Ohridksi”, FMI, IT Content • Objectives • Tasks • Approach • Ontology languages classifications • Ontology languages comparison • RuleML initiative • Conclusions and future work TENCompetence, Sofia, Bulgaria

3. SU “St. Kl. Ohridksi”, FMI, IT Objectives • Investigate how useful Ontologies can be for Knowledgerepresentation and sharing in Learning Networks • Analyse existing technologies and propose a concrete experiment, based on a given ontology TENCompetence, Sofia, Bulgaria

4. SU “St. Kl. Ohridksi”, FMI, IT Ontology • comes from Greekphilosophy and means “the study of thenature of being” • used in theKnowledge Representationdomain “tocategorize the kinds of things existing” • usually is composed of: classes of objects, a vocabulary of terms (instances), and various relations between terms and classes • In Learning Networks we may have domain ontology, pedagogy ontology, and others. TENCompetence, Sofia, Bulgaria

5. SU “St. Kl. Ohridksi”, FMI, IT Tasks • To show how differentdomain ontologies can be used in LearningNetworks for Lifelong CompetenceDevelopment (LN4LCD). • To experiment with reusing ofa given domain ontology in differentLN4LCD. • To analyse the existing ontologylanguages, and to choose a suitable one,supporting the process of reuse of a domainontology. • To show how bestsuch ontology can be incorporated indifferent Learning Designs. TENCompetence, Sofia, Bulgaria

6. SU “St. Kl. Ohridksi”, FMI, IT Approach (1) We have a LN4LCD (2) The Network is using the IMS LD standard (3) There are Units of Learning available, indexed through IMS compliant metadata (4) A target learning domain ontology is available, having information about Units of Learning relations and interdependency, allowing to design abstract, simplified views of training domains. TENCompetence, Sofia, Bulgaria

7. Approach (2) • Each Unit of Learning is linked to theconcepts and relations from the Domainontology, for which itcan be used in learning • Each learner from the LearningNetwork first isassessedto identify whatcompetencies in the domain of interest heposses • This model of the personal competences is mapped to the domain ontology, to achieve newlearner model,being sub-set of the domain ontology. • Comparing the learner model with the competence level thelearner wants to achieve, we should automatically derive a set of concepts andrelations from the domain ontology, whichrepresent the missing knowledge and skillsfor that learner. TENCompetence, Sofia, Bulgaria

8. Approach (3) • Using the model of the gapsin the learner knowledge and skills, and knowing what available Units of Learningwe have, wecan create sets of various possible learningpaths for the learner, which will lead to thecompetence level she/he is aiming. • All thesepossible learning paths can be furtheranalysed depending on different parameters to choose the best suitable learning path for thelearner. TENCompetence, Sofia, Bulgaria

9. SU “St. Kl. Ohridksi”, FMI, IT Main goal of the Approach To find thebest way of separation between generallearning design (expressed as IMS LD package) and domain knowledge (expressedin the domain ontology). This will guaranteethe real interoperability of both the LD andthe domain ontology in different settings andin different LD4LCD. TENCompetence, Sofia, Bulgaria

10. SU “St. Kl. Ohridksi”, FMI, IT Example Domain Ontology • Domain of computer science • Developed in the frame of Diogene project • Knowledge about learning in Computer science is represented by concepts and relations • Concepts based on the ACM classification TENCompetence, Sofia, Bulgaria

11. SU “St. Kl. Ohridksi”, FMI, IT Ontology relations Pre-defined set of threerelations: • HP (Has Part): HP (x, y1, y2, ...., yn) • R (Requires): R (x, y) • SO (Suggested Order):SO (x, y) TENCompetence, Sofia, Bulgaria

12. SU “St. Kl. Ohridksi”, FMI, IT Ontology features • Each concept can be split only byone Has Part relation • The hierarchy defined by these threerelations should not contain loops • The lowest-level concepts areintended to be linked to corresponding Units of Learning • Implemented in Protégé using SHOE TENCompetence, Sofia, Bulgaria

13. SU “St. Kl. Ohridksi”, FMI, IT How to use the ontology • Should support easily operations on domain model and learner model by performing various reasoning operations • All reasoning operations have to be available through the ontology tool • Should be possible to co-exist in a natural way with other ontologies TENCompetence, Sofia, Bulgaria

14. SU “St. Kl. Ohridksi”, FMI, IT Finding the right tool • Analyse and classify existing ontologies and tools • Choose a tool which will allow to perform the experiment for the domain ontology reuse TENCompetence, Sofia, Bulgaria

15. Traditional Ontolingua FLogic OCML Web-based SHOE OIL OWL SWRL Rule-based RuleMl SWRL Ontology Representation languages Onolingua XML UML Ontology interchange languages KIF PIF SU “St. Kl. Ohridksi”, FMI, IT Criteria for ontology languages classification TENCompetence, Sofia, Bulgaria

16. SU “St. Kl. Ohridksi”, FMI, IT Ontology languages classifications TENCompetence, Sofia, Bulgaria

17. Ontology elements Concepts Taxonomy Relations Functions Axioms Instances Production rules Tools Queries Translators Engines Editors User Interfaces SU “St. Kl. Ohridksi”, FMI, IT Criteria for ontology languages selection TENCompetence, Sofia, Bulgaria

18. SU “St. Kl. Ohridksi”, FMI, IT Ontology languages comparison TENCompetence, Sofia, Bulgaria

19. Tool requirements • We need a system which isused both to represent the knowledge in agiven domain, as well as to be able to reason(to make conclusions or to prove relations). • Such a system can be further extended andadapted, so it will be able to select aparticular set of topics from an ontology(representing the learner’s gaps), and toarrange a personalised self-adaptive courseabout the chosen topics (personalisation onthe base of Units of Learning available). TENCompetence, Sofia, Bulgaria

20. SU “St. Kl. Ohridksi”, FMI, IT RuleML initiative • RuleML was chosen for the experiment • RuleML initiative is proposing rules as a natural language for the development of ontologies. • Different languages are designed and can be used in different context, like RuleML, SWRL, RuleML Lite, Object- Oriented RuelML and others. TENCompetence, Sofia, Bulgaria

21. SU “St. Kl. Ohridksi”, FMI, IT RuleML Advantages • Possibilities for markup harmonization, rule syntaxes, rule modules and rule application to be described and properly used. • Extends rule expressiveness and rule semantics, and allows RDF rules and ontology coupling to be used, providing rule validation and rule compilation. • Capabilities to use XML style sheets, semiformal rules and rule documents. • Ability to separate the knowledge and reasoning about given learning domain in one separate tool, to make this independent of the learning design description, logic and use, and in this way to allow real interoperability and reuse both of the Learning Design and the learning domain ontology. TENCompetence, Sofia, Bulgaria

22. SU “St. Kl. Ohridksi”, FMI, IT Conclusion • We choose RuleML (available also through Protégé) as a language and tool for our domain ontology • We will experiment with the reuse of this ontology in different LN4LCD • We will experiment with the co-existence of this ontology with other ontologies (for LD or pedagogy) TENCompetence, Sofia, Bulgaria

23. Future work • Specify the proper set of rules and operations to be used for expressing the domain ontology in RuleML • Experiment with the use of this domain ontology in different LN4LCD for learning path selection • Analyse all possible problems related to the efficiency and performance TENCompetence, Sofia, Bulgaria