<figure><img src="image_1.png" /></figure>

HAL
open science

# A Competency Location System Based Ontology

Leila Zemmouchi-Ghomari, Abdessamed Réda Ghomari, Keltoum Benlahareche

► To cite this version:

Leila Zemmouchi-Ghomari, Abdessamed Réda Ghomari, Keltoum Benlahareche. A Competency Location System Based Ontology. Colloque sur l'Optimisation et les Systèmes d'Information, COSI'2014, Université Abderrahmane Mira, Bejaia, Algérie, Jun 2014, Bejaia, Algeria. hal-04561477

HAL Id: hal-04561477

https://hal.science/hal-04561477

Submitted on 27 Apr 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_1.png" /></figure>

HAL
open science

# A Competency Location System Based Ontology

Leila Zemmouchi-Ghomari, Abdessamed Réda Ghomari, Keltoum Benlahareche

► To cite this version:

Leila Zemmouchi-Ghomari, Abdessamed Réda Ghomari, Keltoum Benlahareche. A Competency Location System Based Ontology. Colloque sur l'Optimisation et les Systèmes d'Information, COSI'2014, Université Abderrahmane Mira, Bejaia, Algérie, Jun 2014, Bejaia, Algeria. hal-04561477

HAL Id: hal-04561477

https://hal.science/hal-04561477v1

Submitted on 27 Apr 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# A Competency Location System Based Ontology

Leila Zemmouchi-Ghomari, Abdessamed Réda Ghomari, Keltoum Benlahareche

LMCS (Laboratoire Méthodes de Conception de Systèmes), ESI (Ecole nationale Supérieure d'Informatique), Oued Smar, 16309, Alger

{1_zemmouchi, a_ghomari, k_benlahareche}@esi.dz

**Abstract:** An essential element in any organization's success is the ability to find quickly the right person for the requested task; that is why organizations need competency location capabilities. Our computer science school (ESI) aims to be proactive to the demands of the market; hence, a cognitive map of intra-organizational skills is required. In this work, we propose to include the semantic perspective to our current competence location system by building an application ontology which will control specifically the used vocabulary and hopefully increase the whole system effectiveness. In order to achieve these goals, we propose a new system architecture of which the most crucial element is an application ontology dedicated to the localization of intra-school skills.

**Keywords:** Competency Location System, Competency Ontology, Application based Ontology.

## 1 Introduction

Organizations need strategies, techniques and tools to connect the people who need to know with those that know. Competency management and knowledge management are currently strategic stakes for every organization. They are at the origins of cooperative strategies founded on collective intelligence [1].

The term competency is derived from the Latin word "competere" which means "to be suitable". In English language there are two similar terms: competence and competency [2]. According to [3], « A competency is the effect of combining and implementing Resources in a specific Context (including physical, social, organizational, cultural and/or economic aspects) for reaching an Objective (or fulfilling a mission) ».

A competency location system implements tools to identify and to organize the areas of knowledge and the expert profiles. This system links any individual seeking information in a particular area with the most competent person in the field. The implementation of such tools optimizes the exchange and the share of tacit knowledge between individuals and increases the intellectual capital (knowledge and skills) of the entire organization. In addition, a competence location system contributes to increasing productivity, improving the quality of work and to optimizing personnel management by reducing the impact of mobility.

In our school ESI (National Superior School of Computer Science), we want to compare profiles of individual persons, the profile of a person with a particular job
# A Competency Location System Based Ontology

Leila Zemmouchi-Ghomari, Abdessamed Réda Ghomari, Keltoum Benlahareche

LMCS (Laboratoire Méthodes de Conception de Systèmes), ESI (Ecole nationale Supérieure d'Informatique), Oued Smar, 16309, Alger

{1_zemmouchi, a_ghomari, k_benlahareche}@esi.dz

**Abstract:** An essential element in any organization's success is the ability to find quickly the right person for the requested task; that is why organizations need competency location capabilities. Our computer science school (ESI) aims to be proactive to the demands of the market; hence, a cognitive map of intra-organizational skills is required. In this work, we propose to include the semantic perspective to our current competence location system by building an application ontology which will control specifically the used vocabulary and hopefully increase the whole system effectiveness. In order to achieve these goals, we propose a new system architecture of which the most crucial element is an application ontology dedicated to the localization of intra-school skills.

**Keywords:** Competency Location System, Competency Ontology, Application based Ontology.

## 1 Introduction

Organizations need strategies, techniques and tools to connect the people who need to know with those that know. Competency management and knowledge management are currently strategic stakes for every organization. They are at the origins of cooperative strategies founded on collective intelligence [1].

The term competency is derived from the Latin word "competere" which means "to be suitable". In English language there are two similar terms: competence and competency [2]. According to [3], « A competency is the effect of combining and implementing Resources in a specific Context (including physical, social, organizational, cultural and/or economic aspects) for reaching an Objective (or fulfilling a mission) ».

A competency location system implements tools to identify and to organize the areas of knowledge and the expert profiles. This system links any individual seeking information in a particular area with the most competent person in the field. The implementation of such tools optimizes the exchange and the share of tacit knowledge between individuals and increases the intellectual capital (knowledge and skills) of the entire organization. In addition, a competence location system contributes to increasing productivity, improving the quality of work and to optimizing personnel management by reducing the impact of mobility.

In our school ESI (National Superior School of Computer Science), we want to compare profiles of individual persons, the profile of a person with a particular job
description or a given training session. Thus, we must either use the same competency definitions or a translation mechanism between different competencies. To decide that the same definition is used, a reference to the definition must be given. A well-defined common understanding of each competence needs to be developed and enforced across various departments or organizations [4]. Promising solutions to these challenges are ontology-based approaches [5], [6].

Two fundamental problems characterize competence location systems:

* The skill model has to be represented in a way that allows all stakeholders to understand the described skills homogeneously by means of formal representations such as ontologies.

* The assessment of skills strength has to be approved broadly. For example, measuring programming skills or soft skills, like: leadership, presentation, negotiating techniques and communication, is really challenging [2].

We think that, the core of an efficient competence location system is an ontology that plays the role of the declarative knowledge repository containing the basic concepts.

In order to address the above issues, we undertook the construction of a competency ontology that aims to represent competencies within a computer science school. This ontology will improve its competency location system by facilitating several tasks such as the comparison between different profiles, the comparison between a profile and a job description. This ontology will also provide unique definitions to all relevant concepts related to the application.

We use the terms “competency”, “competence” or “skill” more or less interchangeably.

In this paper, the emphasis is made on the process of building the application ontology which we consider as the cornerstone of the system that will enhance its global performance. In the second section, we describe some competency ontologies cited in the literature then we present briefly the ontology engineering methodology used to build our ontology, next section is about presenting the new architecture of our intra competency location system in which we plan to integrate the new ontology. Section five details the process of building the application ontology dedicated to represent competency location system of our computer science school, finally we conclude our paper by highlighting what has been achieved, the limitations of our work and what remains to be done.

## 2 Competency Ontologies

A competence location system needs to store dispersed and unstructured corporate knowledge, such as corporate competencies characteristics.

There are taxonomies for job descriptions such as the “Standard Occupational Classification” (www.bls.gov/soc) of the U.S. Department of Labor, which classifies workers into 820 occupations. These categories are useful to identify competency categories related to computer scientists.
description or a given training session. Thus, we must either use the same competency definitions or a translation mechanism between different competencies. To decide that the same definition is used, a reference to the definition must be given. A well-defined common understanding of each competence needs to be developed and enforced across various departments or organizations [4]. Promising solutions to these challenges are ontology-based approaches [5], [6].

Two fundamental problems characterize competence location systems:

* The skill model has to be represented in a way that allows all stakeholders to understand the described skills homogeneously by means of formal representations such as ontologies.

* The assessment of skills strength has to be approved broadly. For example, measuring programming skills or soft skills, like: leadership, presentation, negotiating techniques and communication, is really challenging [2].

We think that, the core of an efficient competence location system is an ontology that plays the role of the declarative knowledge repository containing the basic concepts.

In order to address the above issues, we undertook the construction of a competency ontology that aims to represent competencies within a computer science school. This ontology will improve its competency location system by facilitating several tasks such as the comparison between different profiles, the comparison between a profile and a job description. This ontology will also provide unique definitions to all relevant concepts related to the application.

We use the terms “competency”, “competence” or “skill” more or less interchangeably.

In this paper, the emphasis is made on the process of building the application ontology which we consider as the cornerstone of the system that will enhance its global performance. In the second section, we describe some competency ontologies cited in the literature then we present briefly the ontology engineering methodology used to build our ontology, next section is about presenting the new architecture of our intra competency location system in which we plan to integrate the new ontology. Section five details the process of building the application ontology dedicated to represent competency location system of our computer science school, finally we conclude our paper by highlighting what has been achieved, the limitations of our work and what remains to be done.

## 2 Competency Ontologies

A competence location system needs to store dispersed and unstructured corporate knowledge, such as corporate competencies characteristics.

There are taxonomies for job descriptions such as the “Standard Occupational Classification” (www.bls.gov/soc) of the U.S. Department of Labor, which classifies workers into 820 occupations. These categories are useful to identify competency categories related to computer scientists.
However, as taxonomies are developed for human use; they lack expressive mechanisms to represent, maintain, and reason about complex knowledge in an explicit form and the interpretation is left for human knowledge [7].

In this section, we present the most relevant competency ontologies intended to enhance human resources management, cited in the literature.

* [8] have proposed an ontology for modeling the semantic content of the CVs Jobs in terms of their acquired (CVs) and required (job offer constraints) parts in order to allow them to achieve a subsequent automatic reconciliation. The ontology built allows management skills based on a simplistic model, irrespective of the difference between the identification of skills required by a given job on one hand and the identification and the assessment of skills acquired by an individual on the other hand. In addition, the proposed hierarchy of competency categories has no distinction between the relationship of subsumption "is-a" and the composition or "part of" which is, actually, a modeling error. Finally, a weighting coefficient is associated with each competency category based on its importance in the recruitment process while this importance varies from one business to another.

* [9] has developed a competency management ontology of human resources. This ontology provides support for modeling a vocabulary for the community that share information on human resources. It aims at increasing efficiency in the applied field. Indeed, this ontology provides a common glossary for human experts and software agents involved in the management of human resources. This ontology can be considered as a domain ontology not developed for specific purposes as we target in the present work. However, it can be exploited in the reuse of already consensuated formally represented knowledge about competencies in a CS school.

* [2] have developed an ontology containing the concepts storing, evaluating and reasoning about competencies of students at universities. The system supports students in planning their courses by performing a gap analysis and by producing competency profiles for job applications as job vacancies. Its main concepts are: competency (divided into three categories: computer science, business and behavioral competencies), profile, learning objects. The competencies are structured hierarchically and contain a differentiation between practical and theoretical competencies/knowledge. According to the authors, the most difficult task in such a system is the measurement of competencies. Besides the use of examinations results, the authors found that there are competencies that are not evaluated appropriately at universities. In addition, this work is a first prototype since it considers at the moment only some important competencies.

* [4] proposed a reference model for ontology-based approaches with focus on the development of human resources skills. This conceptual model is built with the idea of integrating management skills in providing learning opportunities for employees. This ontology is called Professional Learning Ontology [10], it provides a shared understanding of different stakeholders integrating previous ontologies developed within other research and industry projects. One of the core concepts of this ontology is the *Learning Opportunity* as an abstraction for human and non-human resources that can foster individual learning processes; this comprises learning objects, presence trainings, checklists and other documents. These learning opportunities can be associated with competencies.
However, as taxonomies are developed for human use; they lack expressive mechanisms to represent, maintain, and reason about complex knowledge in an explicit form and the interpretation is left for human knowledge [7].

In this section, we present the most relevant competency ontologies intended to enhance human resources management, cited in the literature.

* [8] have proposed an ontology for modeling the semantic content of the CVs Jobs in terms of their acquired (CVs) and required (job offer constraints) parts in order to allow them to achieve a subsequent automatic reconciliation. The ontology built allows management skills based on a simplistic model, irrespective of the difference between the identification of skills required by a given job on one hand and the identification and the assessment of skills acquired by an individual on the other hand. In addition, the proposed hierarchy of competency categories has no distinction between the relationship of subsumption "is-a" and the composition or "part of" which is, actually, a modeling error. Finally, a weighting coefficient is associated with each competency category based on its importance in the recruitment process while this importance varies from one business to another.

* [9] has developed a competency management ontology of human resources. This ontology provides support for modeling a vocabulary for the community that share information on human resources. It aims at increasing efficiency in the applied field. Indeed, this ontology provides a common glossary for human experts and software agents involved in the management of human resources. This ontology can be considered as a domain ontology not developed for specific purposes as we target in the present work. However, it can be exploited in the reuse of already consensuated formally represented knowledge about competencies in a CS school.

* [2] have developed an ontology containing the concepts storing, evaluating and reasoning about competencies of students at universities. The system supports students in planning their courses by performing a gap analysis and by producing competency profiles for job applications as job vacancies. Its main concepts are: competency (divided into three categories: computer science, business and behavioral competencies), profile, learning objects. The competencies are structured hierarchically and contain a differentiation between practical and theoretical competencies/knowledge. According to the authors, the most difficult task in such a system is the measurement of competencies. Besides the use of examinations results, the authors found that there are competencies that are not evaluated appropriately at universities. In addition, this work is a first prototype since it considers at the moment only some important competencies.

* [4] proposed a reference model for ontology-based approaches with focus on the development of human resources skills. This conceptual model is built with the idea of integrating management skills in providing learning opportunities for employees. This ontology is called Professional Learning Ontology [10], it provides a shared understanding of different stakeholders integrating previous ontologies developed within other research and industry projects. One of the core concepts of this ontology is the *Learning Opportunity* as an abstraction for human and non-human resources that can foster individual learning processes; this comprises learning objects, presence trainings, checklists and other documents. These learning opportunities can be associated with competencies.
The main difficulty in the reuse of the above ontologies is that; most of them are not available or they have incomplete descriptions (not well documented) that do not permit the evaluation of their usefulness. In fat, all cited ontologies are prototypes or still under construction. This statement leads us to build our own ontology.

## 3 Building Methodology

NeOn Methodology [11] is a scenario-based methodology that provides a set of nine scenarios that can be combined among them. These scenarios can be summarized as follows:

1. *From specification to implementation.* The ontology is developed from scratch

2. *Reusing and re-engineering non-ontological resources.*

3. *Reusing ontological resources.* Developers use ontological primitives to build the new ontology.

4. *Reusing and re-engineering ontological resources.*

5. *Reusing and merging ontological resources.*

6. *Reusing, merging and re-engineering ontological resources.*

7. *Reusing ontology design patterns.*

8. *Restructuring ontological resources.*

9. *Localizing ontological resources.* Ontology developers adapt an ontology to other languages, thus obtaining a multilingual ontology.

The ontology building process is performed according to these phases:

* *Ontology Specification phase* is defined as a collection of requirements that the ontology should fulfill. The output of this activity is the ORSD (Ontology Requirements Specification Document) that includes the purpose, level of formality and scope of the ontology, target group and intended uses of the ontology, and a set of requirements [12]. Neon methodology suggests identifying competency questions as a technique [13] for establishing the ontology requirements.

* *Ontology Conceptualization phase* is defined as an activity in which domain knowledge is structured in a conceptual model that describes the problem and its solution in terms of the domain vocabulary identified in the ontology specification activity. Once the GT (glossary of terms) is completed, terms are grouped as concepts and verbs [14].

* *Ontology Formalization & Implementation phase:* The resulting basic taxonomic structures are then enriched by axioms in formal ontology languages.

## 4 Competency Location System Architecture

A Competency Location System allows identifying within an organization competent individuals in specific domains, according to: their skills, activities, publications [15].

This system has to include the tools to identify and to organize the different areas of knowledge and the corresponding expert profiles in order to match any request
The main difficulty in the reuse of the above ontologies is that; most of them are not available or they have incomplete descriptions (not well documented) that do not permit the evaluation of their usefulness. In fat, all cited ontologies are prototypes or still under construction. This statement leads us to build our own ontology.

## 3 Building Methodology

NeOn Methodology [11] is a scenario-based methodology that provides a set of nine scenarios that can be combined among them. These scenarios can be summarized as follows:

1. *From specification to implementation.* The ontology is developed from scratch

2. *Reusing and re-engineering non-ontological resources.*

3. *Reusing ontological resources.* Developers use ontological primitives to build the new ontology.

4. *Reusing and re-engineering ontological resources.*

5. *Reusing and merging ontological resources.*

6. *Reusing, merging and re-engineering ontological resources.*

7. *Reusing ontology design patterns.*

8. *Restructuring ontological resources.*

9. *Localizing ontological resources.* Ontology developers adapt an ontology to other languages, thus obtaining a multilingual ontology.

The ontology building process is performed according to these phases:

* *Ontology Specification phase* is defined as a collection of requirements that the ontology should fulfill. The output of this activity is the ORSD (Ontology Requirements Specification Document) that includes the purpose, level of formality and scope of the ontology, target group and intended uses of the ontology, and a set of requirements [12]. Neon methodology suggests identifying competency questions as a technique [13] for establishing the ontology requirements.

* *Ontology Conceptualization phase* is defined as an activity in which domain knowledge is structured in a conceptual model that describes the problem and its solution in terms of the domain vocabulary identified in the ontology specification activity. Once the GT (glossary of terms) is completed, terms are grouped as concepts and verbs [14].

* *Ontology Formalization & Implementation phase:* The resulting basic taxonomic structures are then enriched by axioms in formal ontology languages.

## 4 Competency Location System Architecture

A Competency Location System allows identifying within an organization competent individuals in specific domains, according to: their skills, activities, publications [15].

This system has to include the tools to identify and to organize the different areas of knowledge and the corresponding expert profiles in order to match any request
seeking information in a particular area with the most competent person in the field belonging to the organization.

The implementation of competence location tools enables the automation of connecting users. In fact, these tools pursue three objectives:

1. Improve the quality of work: Identification of the most competent person to perform a task, Facilitate Collaboration

2. Improve productivity: Reduce the time required to perform a task, Capitalization of the most frequently asked questions.

3. Improve the management of human capital: Global vision of the available skills in the organization.

Our current competency location system called: *ESI_Clever_Network*, has some shortcomings, namely:

* Input through free text: uncontrolled vocabulary hence the possibility of spelling errors, use of synonyms and ineffective information search,

* Use of keywords or tags: to describe or to annotate the stored data, which leads to a lack of semantics

So in order to address these shortcomings, the system has been enriched with an application ontology for the location of intra organizational skills. This ontology aims to achieve the following 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

The proposed architecture is composed of the following elements, namely:

* A Relational Database: to store all the information reported by users

* A System Interface: home, CV, recommendation, contacts.

* An Application Ontology ECAO: to provide a common vocabulary for members of the organization and assist them with skills reporting at the two levels: Assertional level (A-BOX) and Terminological level (T-BOX)

* An Ontology Editor: to create and to edit the ontology. It also enables the verification of the ontology consistency via reasoners, like: Racer and to query it via SPARQL Editor.

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Competence Location System based ontology Architecture.</figcaption></figure>
seeking information in a particular area with the most competent person in the field belonging to the organization.

The implementation of competence location tools enables the automation of connecting users. In fact, these tools pursue three objectives:

1. Improve the quality of work: Identification of the most competent person to perform a task, Facilitate Collaboration

2. Improve productivity: Reduce the time required to perform a task, Capitalization of the most frequently asked questions.

3. Improve the management of human capital: Global vision of the available skills in the organization.

Our current competency location system called: *ESI_Clever_Network*, has some shortcomings, namely:

* Input through free text: uncontrolled vocabulary hence the possibility of spelling errors, use of synonyms and ineffective information search,

* Use of keywords or tags: to describe or to annotate the stored data, which leads to a lack of semantics

So in order to address these shortcomings, the system has been enriched with an application ontology for the location of intra organizational skills. This ontology aims to achieve the following 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

The proposed architecture is composed of the following elements, namely:

* A Relational Database: to store all the information reported by users

* A System Interface: home, CV, recommendation, contacts.

* An Application Ontology ECAO: to provide a common vocabulary for members of the organization and assist them with skills reporting at the two levels: Assertional level (A-BOX) and Terminological level (T-BOX)

* An Ontology Editor: to create and to edit the ontology. It also enables the verification of the ontology consistency via reasoners, like: Racer and to query it via SPARQL Editor.

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Competence Location System based ontology Architecture.</figcaption></figure>
# 5 Ontology Building Process

We adopted NeOn methodology [12] to build ECAO ontology, "ESI Competence Application Ontology", since it is based on famous ontology engineering methodologies such as: METHONTOLOGY [16], On-To-Knowledge [17] and DILIGENT [18] combined with good practices and feedback from previous experiences of NeOn consortium members. Ontology reuse is recommended in most methodologies in order to develop good quality ontologies since the reused resources are already consensuated knowledge [7]. Therefore, we decided to combine development from scratch (scenario 1) with reuse and reengineering of ontological resources (scenario 4).

Scenario 1: development from scratch where we have to perform the whole ontology engineering process: specification, conceptualization, formalization and implementation without taking into account any existing resources.

Scenario 4: reuse and reengineering of ontological resources involves carrying out the following activities: inverse engineering of ontological resources, restructuring these resources then forward engineering of the resulted structures.

Our application ontology building approach is illustrated by Fig. 2:

<figure><img src="image_4.png" /><figcaption>Fig. 2. Competency Location System based ontology approach.</figcaption></figure>

The proposed approach is carried out according to the following steps: the first two steps are performed in parallel:

1. *Specification*: definition of ontology requirements in the form of competency questions then the extraction of relevant terms in a first glossary

2. *Ontology selection*: finding, evaluating, comparing competency ontologies, and then selecting an ontology among these ontologies.

3. *Reverse engineering* is achieved on the retained ontology in order to derive its conceptual model. Then, terms are extracted from it to constitute the second glossary
# 5 Ontology Building Process

We adopted NeOn methodology [12] to build ECAO ontology, "ESI Competence Application Ontology", since it is based on famous ontology engineering methodologies such as: METHONTOLOGY [16], On-To-Knowledge [17] and DILIGENT [18] combined with good practices and feedback from previous experiences of NeOn consortium members. Ontology reuse is recommended in most methodologies in order to develop good quality ontologies since the reused resources are already consensuated knowledge [7]. Therefore, we decided to combine development from scratch (scenario 1) with reuse and reengineering of ontological resources (scenario 4).

Scenario 1: development from scratch where we have to perform the whole ontology engineering process: specification, conceptualization, formalization and implementation without taking into account any existing resources.

Scenario 4: reuse and reengineering of ontological resources involves carrying out the following activities: inverse engineering of ontological resources, restructuring these resources then forward engineering of the resulted structures.

Our application ontology building approach is illustrated by Fig. 2:

<figure><img src="image_4.png" /><figcaption>Fig. 2. Competency Location System based ontology approach.</figcaption></figure>

The proposed approach is carried out according to the following steps: the first two steps are performed in parallel:

1. *Specification*: definition of ontology requirements in the form of competency questions then the extraction of relevant terms in a first glossary

2. *Ontology selection*: finding, evaluating, comparing competency ontologies, and then selecting an ontology among these ontologies.

3. *Reverse engineering* is achieved on the retained ontology in order to derive its conceptual model. Then, terms are extracted from it to constitute the second glossary
4. *Restructuring*: merging the database data dictionary belonging to the first version of the competency location system with the previously extracted glossaries in order to obtain a global glossary

5. *Forward engineering*: based on the global glossary, the conceptualization phase then the formalization phase are carried out in order to obtain the new ontology

6. *Integration* of the resulted ontology in the first implemented version of the school competency location system

**5.1 Specification**

In this phase, we describe the purpose, the scope, the intended uses, the intended users, the implementation language and the requirements in terms of competency questions (CQs), following the guidelines of the NeOn methodology (table 1).

<caption>**Table 1.** ECAO Ontology Requirement Specification Document (Excerpt)</caption>

**Purpose:** The goal of this ontology is to provide a model of consensual knowledge on skills of our organization's members. **Scope:** This ontology focuses on representing available competences of our school and especially those belonging to the IT field. The level of granularity has to be quite high since we target to build specific and detailed application ontology. **Implementation Language:** OWL 2. **Intended Users:** Graduate students, Teachers, Alumni, Professional staff, such as: experts, trainers. **Intended Uses:** common understanding of used terms, easy and effective skills' search, profiles homogeneity.

The next step is the extraction of relevant terms from competency questions and their potential answers. These terms constitute a glossary of terms and their frequencies, classified according to whether they are nouns, adjectives and verbs. The latter will be formally represented in the ontology. Since the language of the ontology is the French, extracted terms are likewise in French.

**5.2 Ontology Selection**

During this phase, we have to carry out these four activities in order to allow the reuse of ontological resources:

* Activity 1: Discovery of ontologies that meet our system requirements in several repositories and Semantic Web search engines such as Swoogle, Watson and Sindice. We found about twenty five (25) ontologies.

* Activity 2: Evaluation of ontologies. The purpose of this activity is to check if the CQs functional requirements set out in the document ORSD are covered (fully or partially) by the candidate ontologies.

* Activity 3: comparison of ontologies. We took competency questions as the selection criteria of our ontology. Here is an excerpt of candidate ontologies and the number of competency questions they can respond to.
4. *Restructuring*: merging the database data dictionary belonging to the first version of the competency location system with the previously extracted glossaries in order to obtain a global glossary

5. *Forward engineering*: based on the global glossary, the conceptualization phase then the formalization phase are carried out in order to obtain the new ontology

6. *Integration* of the resulted ontology in the first implemented version of the school competency location system

**5.1 Specification**

In this phase, we describe the purpose, the scope, the intended uses, the intended users, the implementation language and the requirements in terms of competency questions (CQs), following the guidelines of the NeOn methodology (table 1).

<caption>**Table 1.** ECAO Ontology Requirement Specification Document (Excerpt)</caption>

**Purpose:** The goal of this ontology is to provide a model of consensual knowledge on skills of our organization's members. **Scope:** This ontology focuses on representing available competences of our school and especially those belonging to the IT field. The level of granularity has to be quite high since we target to build specific and detailed application ontology. **Implementation Language:** OWL 2. **Intended Users:** Graduate students, Teachers, Alumni, Professional staff, such as: experts, trainers. **Intended Uses:** common understanding of used terms, easy and effective skills' search, profiles homogeneity.

The next step is the extraction of relevant terms from competency questions and their potential answers. These terms constitute a glossary of terms and their frequencies, classified according to whether they are nouns, adjectives and verbs. The latter will be formally represented in the ontology. Since the language of the ontology is the French, extracted terms are likewise in French.

**5.2 Ontology Selection**

During this phase, we have to carry out these four activities in order to allow the reuse of ontological resources:

* Activity 1: Discovery of ontologies that meet our system requirements in several repositories and Semantic Web search engines such as Swoogle, Watson and Sindice. We found about twenty five (25) ontologies.

* Activity 2: Evaluation of ontologies. The purpose of this activity is to check if the CQs functional requirements set out in the document ORSD are covered (fully or partially) by the candidate ontologies.

* Activity 3: comparison of ontologies. We took competency questions as the selection criteria of our ontology. Here is an excerpt of candidate ontologies and the number of competency questions they can respond to.
<table><caption>**Table 2.** Candidate Ontologies Comparison</caption><thead><tr><th>Ontology</th><th>O7</th><th>O8</th><th>O20</th><th>O21</th><th>O22</th><th>O24</th><th>O25</th></tr></thead><tbody><tr><th>Number of CQs</th><td>9/40</td><td>7/40</td><td>11/40</td><td>11/40</td><td>13/40</td><td>15/40</td><td>6/40</td></tr></tbody></table>

* Activity 4: selection of ontologies. It appears that O24 [19], among the 25 candidate ontologies, is the closest ontology to our needs materialized by the number of competency questions it can respond to; here is a descriptive card of the selected ontology (table 3).

<caption>**Table 3.** Selected Ontology Descriptive Card</caption>

URI: http://www.institutepupin.com/skills.owl, Label: Skills.owl, Version date: 15/03/2011, Classes: 22, Properties: 17, Instances: 123, Language: OWL. Description: Ontology intended to represent student competency management. It allows students of an institute (Mihajlo Pupin) to express their skills and facilitate their integration into the European Research Area job.

## 5.3 Reverse Engineering

Once the ontology has been selected, we carried out the reverse engineering at the specification phase. Reengineering refers to the process of recovery and transformation of a conceptual model of an existing ontology into a new one [11], by extracting all concepts from this model in order to integrate them in the new conceptual model.

## 5.4 Restructuring

Next step consists in the restructuration or the rearrangement of the retained concepts in order to obtain the concepts of the intended ontology. Actually, we decided to build our ontology from several sources, namely:

* Glossary of terms of the selected ontology

* Glossary of terms obtained from competency questions and their answers

* Data dictionary of the first version of the database of the competency location system

From the three glossaries of terms, we retain all terms even if they appear just in one glossary, because we want to take into account all possible concepts related to our topic of interest.

## 5.5 Forward Engineering

### 5.5.1 Conceptualization

The conceptualization phase [16] consists of organizing and structuring selected terms by using semi-formal intermediate representations that facilitate assimilation of the
<table><caption>**Table 2.** Candidate Ontologies Comparison</caption><thead><tr><th>Ontology</th><th>O7</th><th>O8</th><th>O20</th><th>O21</th><th>O22</th><th>O24</th><th>O25</th></tr></thead><tbody><tr><th>Number of CQs</th><td>9/40</td><td>7/40</td><td>11/40</td><td>11/40</td><td>13/40</td><td>15/40</td><td>6/40</td></tr></tbody></table>

* Activity 4: selection of ontologies. It appears that O24 [19], among the 25 candidate ontologies, is the closest ontology to our needs materialized by the number of competency questions it can respond to; here is a descriptive card of the selected ontology (table 3).

<caption>**Table 3.** Selected Ontology Descriptive Card</caption>

URI: http://www.institutepupin.com/skills.owl, Label: Skills.owl, Version date: 15/03/2011, Classes: 22, Properties: 17, Instances: 123, Language: OWL. Description: Ontology intended to represent student competency management. It allows students of an institute (Mihajlo Pupin) to express their skills and facilitate their integration into the European Research Area job.

## 5.3 Reverse Engineering

Once the ontology has been selected, we carried out the reverse engineering at the specification phase. Reengineering refers to the process of recovery and transformation of a conceptual model of an existing ontology into a new one [11], by extracting all concepts from this model in order to integrate them in the new conceptual model.

## 5.4 Restructuring

Next step consists in the restructuration or the rearrangement of the retained concepts in order to obtain the concepts of the intended ontology. Actually, we decided to build our ontology from several sources, namely:

* Glossary of terms of the selected ontology

* Glossary of terms obtained from competency questions and their answers

* Data dictionary of the first version of the database of the competency location system

From the three glossaries of terms, we retain all terms even if they appear just in one glossary, because we want to take into account all possible concepts related to our topic of interest.

## 5.5 Forward Engineering

### 5.5.1 Conceptualization

The conceptualization phase [16] consists of organizing and structuring selected terms by using semi-formal intermediate representations that facilitate assimilation of the
new representation and are totally independent of any implementation language. This phase consists of several parts: Data dictionary, Classification of concepts (Fig 3), Table of binary relations, Table of attributes, Table of logical axioms and Table of instances. 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

Besides, we have to distinguish theoretical knowledge from practical experience. The evidence for a theoretical competency may be a course or a certificate of training. Activities carried out by the experts are categorized according to whether they are: Teaching, Supervision, Research Work such as publishing, participating in a project or organizing a seminar, Administrative work and Business activity.

The key concepts of our ontology are illustrated in Fig. 3. Ontology concepts are ordered hierarchically, but moreover, additional relations describe links and constraints between concepts supporting consistency mechanisms and operations.

<figure><img src="image_5.png" /><figcaption>Fig. 3 Key Concepts of ECAO Ontology.</figcaption></figure>

### 5.5.2 Formalization

Formal ontology must include axioms using formal language to constrain the possible interpretations of the ontology components. Since OWL is based on Description Logics, we used it to express property restrictions. These restrictions are used to limit the individuals belonging to a single class and contain anonymous classes that satisfy those limits. For example: an expert has some professional experience, has practiced some activity and has obtained one degree as a minimum.
new representation and are totally independent of any implementation language. This phase consists of several parts: Data dictionary, Classification of concepts (Fig 3), Table of binary relations, Table of attributes, Table of logical axioms and Table of instances. 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

Besides, we have to distinguish theoretical knowledge from practical experience. The evidence for a theoretical competency may be a course or a certificate of training. Activities carried out by the experts are categorized according to whether they are: Teaching, Supervision, Research Work such as publishing, participating in a project or organizing a seminar, Administrative work and Business activity.

The key concepts of our ontology are illustrated in Fig. 3. Ontology concepts are ordered hierarchically, but moreover, additional relations describe links and constraints between concepts supporting consistency mechanisms and operations.

<figure><img src="image_5.png" /><figcaption>Fig. 3 Key Concepts of ECAO Ontology.</figcaption></figure>

### 5.5.2 Formalization

Formal ontology must include axioms using formal language to constrain the possible interpretations of the ontology components. Since OWL is based on Description Logics, we used it to express property restrictions. These restrictions are used to limit the individuals belonging to a single class and contain anonymous classes that satisfy those limits. For example: an expert has some professional experience, has practiced some activity and has obtained one degree as a minimum.
ECAO ontology has been implemented in OWL 2 DL profile produced by Neon Toolkit editor. The resulting ontology is available at: https://sourceforge.net/projects/competenyapplicationontology/ and at: http://datahub.io/fr/dataset/competency-application-ontology

In terms of ontology evaluation, we know that reasoners can help to maintain the hierarchy correctly [20]. In fact, this type of assessment is called a structural evaluation that considers the logical structure of the ontology. After submitting ECAO ontology to several reasoners, such as: Pellet, FaCT++, hermit and Racer, neither inconsistency nor redundancy has been discovered.

## 5.6 Integration

The integration of ECAO ontology in the competence location system includes:

* Modifications in the structure of the database, such as adding missing tables: competency table, formation table and programming the new constraints on database entities such as disjointness between competency categories.

* Modifications in the application interface such as to replace the free text input fields with extensible lists of predefined values. These lists can limit the risk of errors when entering data (Fig. 5).

<figure><img src="image_6.png" /><figcaption>Fig. 5. Competence Location System Interface.</figcaption></figure>

This integration required the use of a tool: RAP-RDF API for PHP (http://wifo5-03.informatik.uni-mannheim.de/bizer/rdfapi) that allows the manipulation, the analysis and the querying of the ontology from a PHP document. However, this operation is still in progress and the application isn't yet fully exploited.

# 6 Conclusion

Definitely, the integration of the semantic dimension in the context of our school competence location system will enable a global vision of the available skills in the organization and hence enable a better identification of the most competent person to perform a given task and facilitate collaboration.
ECAO ontology has been implemented in OWL 2 DL profile produced by Neon Toolkit editor. The resulting ontology is available at: https://sourceforge.net/projects/competenyapplicationontology/ and at: http://datahub.io/fr/dataset/competency-application-ontology

In terms of ontology evaluation, we know that reasoners can help to maintain the hierarchy correctly [20]. In fact, this type of assessment is called a structural evaluation that considers the logical structure of the ontology. After submitting ECAO ontology to several reasoners, such as: Pellet, FaCT++, hermit and Racer, neither inconsistency nor redundancy has been discovered.

## 5.6 Integration

The integration of ECAO ontology in the competence location system includes:

* Modifications in the structure of the database, such as adding missing tables: competency table, formation table and programming the new constraints on database entities such as disjointness between competency categories.

* Modifications in the application interface such as to replace the free text input fields with extensible lists of predefined values. These lists can limit the risk of errors when entering data (Fig. 5).

<figure><img src="image_6.png" /><figcaption>Fig. 5. Competence Location System Interface.</figcaption></figure>

This integration required the use of a tool: RAP-RDF API for PHP (http://wifo5-03.informatik.uni-mannheim.de/bizer/rdfapi) that allows the manipulation, the analysis and the querying of the ontology from a PHP document. However, this operation is still in progress and the application isn't yet fully exploited.

## 6 Conclusion

Definitely, the integration of the semantic dimension in the context of our school competence location system will enable a global vision of the available skills in the organization and hence enable a better identification of the most competent person to perform a given task and facilitate collaboration.
Based on these expectations, we designed a new architecture of our competency
location system then we undertook the construction of a competency application
ontology to be integrated in it. This structure has been developed by combining two
scenarios among the nine scenarios proposed by NeOn methodology, namely:
development from scratch (scenario 1) with reuse and reengineering of ontological
resources (scenario 4) in order to achieve a correct coverage of relevant concepts
describing the knowledge of our domain of interest.

The integration of the application ontology in the system has to be completely
achieved in order to perform an evaluation of the efficiency of the new version of the
system in comparison with its previous version.

Moreover, there is an issue of ontology sustainability, in other terms, how to keep
the ontology up-to-date with the competence domain so that it actually reflects what is
really needed.

However, we are confident that ontologies are able to play a significant role in
enhancing the overall performance of such software applications.

References

1. Cornescu, V., Marinescu, P., Curteanu, D. & Toma, S: Managementul resurselor umane, Popescu (Ed) Management de la teorie la prectică. University of Bucharest, Bucharest, Romania (2004).

2. Dorn, J. & Pichlmair, M.: A competence management system for Universities. In: Proceedings of the European conference on artificial intelligence, St Gallen, Switzerland (2007)

3. Trichet, M.: A Framework for Building Competency-Based Systems Dedicated to human Resource Management. In: N. Zhong et al. (Eds.), ISMIS 2003, LNAI 2871, pp. 633--639 (2003)

4. Schmidt, A. & Kunzmann, C.: Sustainable Competency-Oriented Human Resource Development with Ontology-Based Competency Catalogs. In: proceedings of E-Challenges, The Hague, The Netherlands (2007)

5. Biesalski, E. & Abecker, A.: Human Resource Management with Ontologies. In: the 3rd Conference on Professional Knowledge Management-Experience and Visions, Kaiserslautern, Germany (2005)

6. Laukkanen, M. & Helin, H.: Competence Manager—Applying the Semantic Web in Practice. In: the third International Semantic Web Conference, ISWC, Hiroshima, Japan (2004)

7. Zemmouchi-Ghomari, L. & Ghomari, A.R. : Terminologies versus Ontologies from the perspective of ontologists. In: International Journal of Web Science, Inderscience Publisher, Vol. 1, no. 4, pp. 315—331 (2012)

8. Amourache F., Boufaida Z. & Yahiaoui L. : Construction D'une Ontologie Basée Compétence pour l'annotation des Cvs/ Offres d'emploi. In: the 10th Conference on Software Engineering and Artificial Intelligence (MCSEAI), Maghrebian Conference on Information Technologies, Oran, Algérie (2008)

9. Szekely, A.: An Approach to Ontology Development in Human Resources Management. In: the 5th International Conference on Virtual Learning ICVL, Târgu-Mures, Romania (2010)

10. Schmidt, A. & Kunzmann, C.: Towards a Human Resource Development Ontology for Combining Competence Management and Technology-Enhanced Workplace Learning. In:
Based on these expectations, we designed a new architecture of our competency
location system then we undertook the construction of a competency application
ontology to be integrated in it. This structure has been developed by combining two
scenarios among the nine scenarios proposed by NeOn methodology, namely:
development from scratch (scenario 1) with reuse and reengineering of ontological
resources (scenario 4) in order to achieve a correct coverage of relevant concepts
describing the knowledge of our domain of interest.

The integration of the application ontology in the system has to be completely
achieved in order to perform an evaluation of the efficiency of the new version of the
system in comparison with its previous version.

Moreover, there is an issue of ontology sustainability, in other terms, how to keep
the ontology up-to-date with the competence domain so that it actually reflects what is
really needed.

However, we are confident that ontologies are able to play a significant role in
enhancing the overall performance of such software applications.

References

1. Cornescu, V., Marinescu, P., Curteanu, D. & Toma, S: Managementul resurselor umane, Popescu (Ed) Management de la teorie la prectică. University of Bucharest, Bucharest, Romania (2004).

2. Dorn, J. & Pichlmair, M.: A competence management system for Universities. In: Proceedings of the European conference on artificial intelligence, St Gallen, Switzerland (2007)

3. Trichet, M.: A Framework for Building Competency-Based Systems Dedicated to human Resource Management. In: N. Zhong et al. (Eds.), ISMIS 2003, LNAI 2871, pp. 633--639 (2003)

4. Schmidt, A. & Kunzmann, C.: Sustainable Competency-Oriented Human Resource Development with Ontology-Based Competency Catalogs. In: proceedings of E-Challenges, The Hague, The Netherlands (2007)

5. Biesalski, E. & Abecker, A.: Human Resource Management with Ontologies. In: the 3rd Conference on Professional Knowledge Management-Experience and Visions, Kaiserslautern, Germany (2005)

6. Laukkanen, M. & Helin, H.: Competence Manager—Applying the Semantic Web in Practice. In: the third International Semantic Web Conference, ISWC, Hiroshima, Japan (2004)

7. Zemmouchi-Ghomari, L. & Ghomari, A.R. : Terminologies versus Ontologies from the perspective of ontologists. In: International Journal of Web Science, Inderscience Publisher, Vol. 1, no. 4, pp. 315—331 (2012)

8. Amourache F., Boufaida Z. & Yahiaoui L. : Construction D'une Ontologie Basée Compétence pour l'annotation des Cvs/ Offres d'emploi. In: the 10th Conference on Software Engineering and Artificial Intelligence (MCSEAI), Maghrebian Conference on Information Technologies, Oran, Algérie (2008)

9. Szekely, A.: An Approach to Ontology Development in Human Resources Management. In: the 5th International Conference on Virtual Learning ICVL, Târgu-Mures, Romania (2010)

10. Schmidt, A. & Kunzmann, C.: Towards a Human Resource Development Ontology for Combining Competence Management and Technology-Enhanced Workplace Learning. In:
the first Workshop on Ontology Content and Evaluation in Enterprise, OntoContent, pp. 1078--1087, Montpellier, France (2006)

11. Suárez-Figueroa, M., Gómez-Pérez, A. & Fernández-López, M.: The NeOn Methodology for Ontology Engineering. In: Ontology Engineering in a Networked World, pp. 9--34, Springer, Berlin Heidelberg, Germany (2012)

12. Suárez-Figueroa, M.C, Gomez-Perez A. & Villazon-Terrazas, B.: How to write and use the Ontology Requirements Specification Document. In: Proceedings of International Conference on Ontologies, DataBases, and Applications of Semantics ODBASE, Algarve, Portugal pp. 966--982 (2009)

13. Gruninger, M. & Fox, M.S.: Methodology for the design and evaluation of ontologies. In: Workshop on Basic Ontological Issues in Knowledge Sharing, pp. 6.1--6.10, Montreal, Canada (1995)

14. Fernandez-Lopez, M. Gomez-Perez, A. & Juristo, N.: METHONTOLOGY: From Ontological Art Towards Ontological Engineering. In: AAAI Symposium on Ontological Engineering, Stanford, USA (1997)

15. Balmisse G.: La Localisation d'Expertise, Introduction, Livre Blanc Knowledge Consulting, Paris, France (2003)

16. Gomez-Perez, A.: Towards a method to conceptualize domain ontologies. In: Workshop on Ontological Engineering, ECAI '96, pp. 41--52, Budapest, Hungary (1996)

17. Sure, Y. & Studer, R.: On-To-Knowledge methodology. In : Davies, J. et al. (eds.) On-To-Knowledge: Semantic Web enabled Knowledge Management. J. Wiley and Sons, Norwich, UK (2002)

18. Pinto, S., Staab, S. & Tempich, C.: Diligent Towards a fine-grained methodology for distributed, loosely-controlled and evolving engineering of ontologies. In: Proceedings of ECAI, the 16th European Conference on Artificial Intelligence, pp. 393--397, Valencia, Spain (2004)

19. Janev. V & Sanja. V.: Ontology-based Competency Management: the Case Study of the Mihajlo Pupin Institute. In: Journal of Universal Computer Science, vol. 17, no. 7, pp.1089--1108 (2011)

20. Horridge, M., Jupp, S., Moulton, G., Rector, A., Stevens, R., Wroe, C.: A Practical Guide To Building OWL Ontologies Using Protege 4 and CO-ODE Tools, Technical Report, The University Of Manchester, Manchester, UK (2007)
the first Workshop on Ontology Content and Evaluation in Enterprise, OntoContent, pp. 1078--1087, Montpellier, France (2006)

11. Suárez-Figueroa, M., Gómez-Pérez, A. & Fernández-López, M.: The NeOn Methodology for Ontology Engineering. In: Ontology Engineering in a Networked World, pp. 9--34, Springer, Berlin Heidelberg, Germany (2012)

12. Suárez-Figueroa, M.C, Gomez-Perez A. & Villazon-Terrazas, B.: How to write and use the Ontology Requirements Specification Document. In: Proceedings of International Conference on Ontologies, DataBases, and Applications of Semantics ODBASE, Algarve, Portugal pp. 966--982 (2009)

13. Gruninger, M. & Fox, M.S.: Methodology for the design and evaluation of ontologies. In: Workshop on Basic Ontological Issues in Knowledge Sharing, pp. 6.1--6.10, Montreal, Canada (1995)

14. Fernandez-Lopez, M. Gomez-Perez, A. & Juristo, N.: METHONTOLOGY: From Ontological Art Towards Ontological Engineering. In: AAAI Symposium on Ontological Engineering, Stanford, USA (1997)

15. Balmisse G.: La Localisation d'Expertise, Introduction, Livre Blanc Knowledge Consulting, Paris, France (2003)

16. Gomez-Perez, A.: Towards a method to conceptualize domain ontologies. In: Workshop on Ontological Engineering, ECAI '96, pp. 41--52, Budapest, Hungary (1996)

17. Sure, Y. & Studer, R.: On-To-Knowledge methodology. In : Davies, J. et al. (eds.) On-To-Knowledge: Semantic Web enabled Knowledge Management. J. Wiley and Sons, Norwich, UK (2002)

18. Pinto, S., Staab, S. & Tempich, C.: Diligent Towards a fine-grained methodology for distributed, loosely-controlled and evolving engineering of ontologies. In: Proceedings of ECAI, the 16th European Conference on Artificial Intelligence, pp. 393--397, Valencia, Spain (2004)

19. Janev. V & Sanja. V.: Ontology-based Competency Management: the Case Study of the Mihajlo Pupin Institute. In: Journal of Universal Computer Science, vol. 17, no. 7, pp.1089--1108 (2011)

20. Horridge, M., Jupp, S., Moulton, G., Rector, A., Stevens, R., Wroe, C.: A Practical Guide To Building OWL Ontologies Using Protege 4 and CO-ODE Tools, Technical Report, The University Of Manchester, Manchester, UK (2007)