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Ph.D de

Ph.D
Group :

Digital Identity Discovery and Reconciliation for Human Resources Management

Starts on 01/04/2014
Advisor : SEGHOUANI BENNACER, Nacéra
[QUERCINI Gianluca]

Funding : Contrat doctoral uniquement recherche
Affiliation : Centrale Supélec
Laboratory :

Defended on 27/11/2017, committee :
- Patrick MARCEL – François-Rabelais Université

- Mathieu ROCHE – CIRAD

- Nacéra SEGHOUANI BENNACER – LRI, CentraleSupélec

- Gianluca QUERCINI – LRI, CentraleSupélec

- Dario COLAZZO – Paris-Dauphine Université

- Nicolas SABOURET – Paris-Sud Université

- Florent ANDRÉ – MindMatcher

Research activities :

Abstract :
Finding the appropriate individual to hire is a crucial part of any organization. With the number of applications increasing due to the introduction of online job portals, it is desired to automatically match applicants with job offers. Existing approaches that match applicants with job offers take resumes as they are and do not attempt to complete the information on a resume by looking for more information on the Internet. The objective of this thesis is to fill this gap by discovering online resources pertinent to an applicant. To this end, a novel method for extraction of key information from resumes is proposed. This is a challenging task since resumes can have diverse structures and formats, and the entities present within are ambiguous. Identification of Web results using the key information and their reconciliation is another challenge. We propose an algorithm to generate queries, and rank the results to obtain the most pertinent online resources. In addition, we specifically tackle reconciliation of social network profiles through a method that is able to identify profiles of individuals across different networks. Moreover, a method to resolve ambiguity in locations, or predict it when absent, is also presented. Experiments on real data sets are conducted for all the different algorithms proposed in this thesis and they show good results.