Vers un cadre ontologique pour la gestion des comp{é}tences : {à} des fins de formation, de recrutement, de m{é}tier, ou de recherches associ{é}es

• URL: http://arxiv.org/abs/2507.05767v1
• Date: Tue, 08 Jul 2025 08:13:30 GMT
• Title: Vers un cadre ontologique pour la gestion des comp{é}tences : {à} des fins de formation, de recrutement, de m{é}tier, ou de recherches associ{é}es
• Authors: Ngoc Luyen Le, Marie-Hélène Abel, Bertrand Laforge,
• Abstract summary: This paper proposes an ontological-based framework for competence management.<n>It enables a structured representation of competencies, occupations, and training programs.<n>It aims to enhance the automation of competence-to-job matching, the personalization of learning recommendations, and career planning.
• Score: 32.26033017109275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid transformation of the labor market, driven by technological advancements and the digital economy, requires continuous competence development and constant adaptation. In this context, traditional competence management systems lack interoperability, adaptability, and semantic understanding, making it difficult to align individual competencies with labor market needs and training programs. This paper proposes an ontology-based framework for competence management, enabling a structured representation of competencies, occupations, and training programs. By leveraging ontological models and semantic reasoning, this framework aims to enhance the automation of competence-to-job matching, the personalization of learning recommendations, and career planning. This study discusses the design, implementation, and potential applications of the framework, focusing on competence training programs, job searching, and finding competent individuals.

Related papers

• Beyond the Classroom: Bridging the Gap Between Academia and Industry with a Hands-on Learning Approach [2.8123958518740544]
  Self-adaptive software systems have emerged as a critical focus in software design and operation.<n>A survey among practitioners identified that the lack of knowledgeable individuals has hindered its adoption in the industry.<n>We present our experience teaching a course on self-adaptive software systems that integrates theoretical knowledge and hands-on learning with industry-relevant technologies.
  arXiv  Detail & Related papers  (2025-04-14T21:32:25Z)
• Generative AI Literacy: Twelve Defining Competencies [48.90506360377104]
  This paper introduces a competency-based model for generative artificial intelligence (AI) literacy covering essential skills and knowledge areas necessary to interact with generative AI.<n>The competencies range from foundational AI literacy to prompt engineering and programming skills, including ethical and legal considerations.<n>These twelve competencies offer a framework for individuals, policymakers, government officials, and educators looking to navigate and take advantage of the potential of generative AI responsibly.
  arXiv  Detail & Related papers  (2024-11-29T14:55:15Z)
• Unveiling the Skills and Responsibilities of Serverless Practitioners: An Empirical Investigation [0.3455674031934434]
  This study aims to identify and organize the industry requirements for serverless practitioners by conducting a qualitative analysis of 141 job advertisements from seven countries. We developed comprehensive roles, responsibilities, and skills, categorizing 19 responsibilities into four themes: software development, infrastructure and operations, professional development and leadership, and software business. We identified 28 hard skills mapped into seven themes and 32 soft skills mapped into eight themes, with the six most demanded soft skills being communication proficiency, continuous learning and adaptability, collaborative teamwork, problem-solving and analytical skills, leadership excellence, and project management.
  arXiv  Detail & Related papers  (2024-11-15T16:45:04Z)
• Latent-Predictive Empowerment: Measuring Empowerment without a Simulator [56.53777237504011]
  We present Latent-Predictive Empowerment (LPE), an algorithm that can compute empowerment in a more practical manner. LPE learns large skillsets by maximizing an objective that is a principled replacement for the mutual information between skills and states.
  arXiv  Detail & Related papers  (2024-10-15T00:41:18Z)
• Social Skill Training with Large Language Models [65.40795606463101]
  People rely on social skills like conflict resolution to communicate effectively and to thrive in both work and personal life. This perspective paper identifies social skill barriers to enter specialized fields. We present a solution that leverages large language models for social skill training via a generic framework.
  arXiv  Detail & Related papers  (2024-04-05T16:29:58Z)
• Decoding excellence: Mapping the demand for psychological traits of operations and supply chain professionals through text mining [0.0]
  This study proposes an innovative methodology for the profiling of psychological traits of Operations Management (OM) and Supply Chain Management (SCM) professionals. We use innovative methods and tools of text mining and social network analysis to map the demand for relevant skills from a set of job descriptions.
  arXiv  Detail & Related papers  (2024-03-26T09:51:43Z)
• An Actionable Framework for Understanding and Improving Talent Retention as a Competitive Advantage in IT Organizations [44.342141516382284]
  This work presents an actionable framework for Talent Retention (TR) used in IT organizations. Our framework encompasses a set of factors, contextual characteristics, barriers, strategies, and coping mechanisms. Our findings indicated that software engineers can be differentiated from other professional groups.
  arXiv  Detail & Related papers  (2024-02-02T17:08:14Z)
• Modelling Assessment Rubrics through Bayesian Networks: a Pragmatic Approach [40.06500618820166]
  This paper presents an approach to deriving a learner model directly from an assessment rubric. We illustrate how the approach can be applied to automatize the human assessment of an activity developed for testing computational thinking skills.
  arXiv  Detail & Related papers  (2022-09-07T10:09:12Z)
• Autonomous Open-Ended Learning of Tasks with Non-Stationary Interdependencies [64.0476282000118]
  Intrinsic motivations have proven to generate a task-agnostic signal to properly allocate the training time amongst goals. While the majority of works in the field of intrinsically motivated open-ended learning focus on scenarios where goals are independent from each other, only few of them studied the autonomous acquisition of interdependent tasks. In particular, we first deepen the analysis of a previous system, showing the importance of incorporating information about the relationships between tasks at a higher level of the architecture. Then we introduce H-GRAIL, a new system that extends the previous one by adding a new learning layer to store the autonomously acquired sequences
  arXiv  Detail & Related papers  (2022-05-16T10:43:01Z)
• Competence Assessment as an Expert System for Human Resource Management: A Mathematical Approach [5.753758386996188]
  This article describes the combined use of software technologies and mathematical and statistical methods for assessing and analyzing competences in human resource information systems. The system has been tested with real human resource data sets in the frame of the European project ComProFITS.
  arXiv  Detail & Related papers  (2020-01-16T21:37:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.