A competency location system based ontology presentation
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
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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
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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
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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
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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)
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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
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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
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Phase 5: Forward Engineering
Phase 5.1: Conceptualization
Consists of organizing and structuring relevant terms
Ontology Building Process
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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
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Ontology Building Process
Phase 5.2: Formalization
Formal ontology must include axioms using formal
language to constrain the possible interpretations of
the ontology components
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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
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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)
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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
