No abstract

Over the last decade, several e-recruitment platforms have been developed, allowing users to publish their professional information (training, work history, career summary, etc.). However, representing this huge quantity of knowledge still limited. In this work, we present a method based on community detection and natural language processing techniques in order to generate a human resources “HR” ontology. The data used in the generation process is user’s profiles retrieved from the Algerian e-recruitment platform Emploitic.com ( www.emploitic.com ). Data includes occupations, skills and professional domains. Our main contribution appears in the identification of new relationships between these concepts using community detection in each area of work. The generated ontology has hierarchical relationships between skills, professions and professional domains. In order to evaluate the relevance of this ontology, we used both the manual method with experts in human resources domain and the automatic method through comparisons with existing HR-ontologies. The evaluation has shown promising results.

The emergence of new communication technologies and the latest generation of smart-phone devices, allow the user to continuously access the Web, at any time and from any location with different devices. The introduction of the user and his environment in the research process is intended to solve the information overload problem and to increase the accuracy of the retrieval system. In this paper, we present a new approach to select the best sources from different heterogeneous sources by exploiting a multidimensional contextual user profile in a mobile environment, that includes the research situation "time, location ...", the device and the user preferences. We also present our approach to define user and source profiles, using external ontologies and a learning algorithm supervised by user results satisfaction score.