A competency location system based ontology presentation
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A competency location system based ontology presentation

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it is our presentation at COSI'2014 (Colloque sur l'Optimisation et les Systèmes d'Information), 8-10 June 2014, Béjaia, Algérie

it is our presentation at COSI'2014 (Colloque sur l'Optimisation et les Systèmes d'Information), 8-10 June 2014, Béjaia, Algérie

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A competency location system based ontology presentation Presentation Transcript

  • 1. Authors: Leila ZEMMOUCHI-GHOMARI, l_zemmouchi@esi.dz Abdessamed Réda GHOMARI, a_ghomari@esi.dz Keltoum Benlahareche, k_benlahreche@esi.dz LMCS Laboratory ESI, national Superior School of Computer Science, www.esi.dz Algiers, ALGERIA Colloque sur l'Optimisation et les Systèmes d'Information COSI'2014, 8-10 Juin 2014, Béjaia, Algérie Université Abderrahmane Mira - Béjaia
  • 2. 10/06/2014COSI' 2014 2 Motivation Right PersonA given Task Organization
  • 3. 10/06/2014COSI' 2014 3 Competency location system objectives: 1. Improve the quality of work: Identification of the most competent person to perform a task 2. Improve the productivity: Reduce the time required to perform a task 3. Improve the management of the human capital: Global vision of the available skills in the Organization Motivation
  • 4. 10/06/2014COSI' 2014 4 Motivation Our current CLS has some shortcomings: • Input through free text: possibility of spelling errors, use of synonyms and ineffective information search • Exclusive use of tags to describe the stored data, which leads to a lack of semantics
  • 5. 10/06/2014COSI' 2014 5 in order to address these shortcomings, the system has to be enhanced with an application ontology for the location of intra organizational skills Expected ontology benefits: • Use of a controlled vocabulary: same vocabulary for all members of the organization • Enrichment of terms with semantics, efficient skills research and a better management skills Proposed Solution
  • 6. 10/06/2014COSI' 2014 6 The new architecture of the competency location system Proposed Solution
  • 7. 10/06/2014COSI' 2014 7 Ontology Building Process We adopted NeOn methodology [Suárez-Figueroa, 2012] to build ECAO ontology, "ESI Competence Application Ontology" We combined two scenarios(from 9 scenarios): 1. Development from scratch (scenario 1): specification, conceptualization and formalization 2. Reuse and Reengineering of ontological resources (scenario 4)
  • 8. 10/06/2014COSI' 2014 8 Ontology Building Process
  • 9. 10/06/2014COSI' 2014 9 Phase 1: Specification • Produce Ontology Requirements Specification Document (ORSD)  Purpose, scope, intended users, intended uses, Implementation Language, list of competency questions (ontology requirements) • Extract relevant terms from Competency questions and their answers  Glossary of terms Ontology Building Process
  • 10. 10/06/2014COSI' 2014 10 ECAO Competeny Questions
  • 11. 10/06/2014COSI' 2014 11 Ontology Building Process
  • 12. 10/06/2014COSI' 2014 12 Phase 2: Ontology Selection - Discovery in repositories and SW search engines:  25 candidate ontologies - Evaluation & comparison: are the requirements (CQs) covered by these ontologies? - Selection of O24: URI: www.institutepupin.com/skills.owl label: skills.owl, version: 2011 classes, properties ans instances: 22/17/123 Ontology Building Process
  • 13. 10/06/2014COSI' 2014 13 Ontology Building Process
  • 14. 10/06/2014COSI' 2014 14 Phase 3: Reverse Engineering Ontology Building Process
  • 15. 10/06/2014COSI' 2014 15 Ontology Building Process The conceptual model of the selected ontology/Extracted terms
  • 16. 10/06/2014COSI' 2014 16 Ontology Building Process
  • 17. 10/06/2014COSI' 2014 17 1. Glossary of terms of the selected ontology 2. Glossary of terms obtained from competency questions and their answers 3. Data dictionary of the first version of the database of “ESI Clever Network” Ontology Building Process Phase 4: Restructuring Fusion of the terms of the following glossaries
  • 18. 10/06/2014COSI' 2014 18 Ontology Building Process
  • 19. 10/06/2014COSI' 2014 19 Phase 5: Forward Engineering Phase 5.1: Conceptualization Consists of organizing and structuring relevant terms Ontology Building Process
  • 20. 10/06/2014COSI' 2014 20 Typical competencies to be modeled in our domain are: • Technical abilities, such as programming languages, database management systems, operating systems or optimization tools • Engineering competences, such as networking, computer architecture, human-computer interaction or knowledge management • Social competences, such as coaching, collaboration or communication • Language skills such as writing, reading or speaking • Business competences such as auditing, management or selling ECAO Ontology
  • 21. 10/06/2014COSI' 2014 21 Ontology Building Process
  • 22. 10/06/2014COSI' 2014 22 Ontology Building Process Phase 5.2: Formalization Formal ontology must include axioms using formal language to constrain the possible interpretations of the ontology components
  • 23. 10/06/2014COSI' 2014 23 Available at: https://sourceforge.net/projects/competenyapplicationontology/ and at: http://datahub.io/fr/dataset/competency-application-ontology ECAO ontology has been implemented in produced by Neon Toolkit editor ECAO Ontology
  • 24. 10/06/2014COSI' 2014 24 Ontology Building Process
  • 25. 10/06/2014COSI' 2014 25 The integration of ECAO ontology in the competence location system includes: • Update the CLS Database according to the new conceptual schema, such as adding missing tables and programming the new constraints on the database • Update the CLS program according to the ontology constraints, such as to replace the free text input fields with extensible lists of predefined values … New CLS based Ontology Phase 6: Integration (ongoing work)
  • 26. 10/06/2014COSI' 2014 26 What We did: • we designed a new architecture of our current competency location system (add the semantic perspective) • we developed a competency application ontology (best practices of ontology development) to be integrated in the CLS What remains to be done: • To Integrate the developed ontology in the CLS • To Evaluate the new CLS Conclusion & future work
  • 27. 10/06/2014COSI' 2014 27