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Automatic Human Resources Ontology Generation from the Data of an E-Recruitment Platform

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Metadata and Semantic Research (MTSR 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1355))

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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.

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Notes

  1. 1.

    Algerian Human Resources Ontology Generated Automatically.

  2. 2.

    https://www.w3.org/OWL/.

References

  1. Alfonso-Hermelo, D., Langlais, P., Bourg, L.: Automatically learning a human-resource ontology from professional social-network data. In: Meurs, M.-J., Rudzicz, F. (eds.) Canadian AI 2019. LNCS (LNAI), vol. 11489, pp. 132–145. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-18305-9_11

    Chapter  Google Scholar 

  2. Bedini, I., Nguyen, B.: Automatic ontology generation: state of the art. PRiSM Laboratory Technical report. University of Versailles (2007)

    Google Scholar 

  3. Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008(10), P10008 (2008)

    Article  Google Scholar 

  4. Cohen, W.W., Ravikumar, P., Fienberg, S.E., et al.: A comparison of string distance metrics for name-matching tasks. In: IIWeb 2003, pp. 73–78 (2003)

    Google Scholar 

  5. Jaro, M.A.: Advances in record-linkage methodology as applied to matching the 1985 census of Tampa, Florida. J. Am. Stat. Assoc. 84(406), 414–420 (1989)

    Article  Google Scholar 

  6. Kessler, R., Lapalme, G.: Agohra: generation of an ontology in the field of human resources. Traitement Automatique des Langues 58(1), 39–62 (2017)

    Google Scholar 

  7. Omelayenko, B., Fensel, D.: A two-layered integration approach for product information in B2B E-commerce. In: Bauknecht, K., Madria, S.K., Pernul, G. (eds.) EC-Web 2001. LNCS, vol. 2115, pp. 226–239. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44700-8_22

    Chapter  MATH  Google Scholar 

  8. Orman, G.K., Labatut, V.: A comparison of community detection algorithms on artificial networks. In: Gama, J., Costa, V.S., Jorge, A.M., Brazdil, P.B. (eds.) DS 2009. LNCS (LNAI), vol. 5808, pp. 242–256. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04747-3_20

    Chapter  Google Scholar 

  9. Rish, I., et al.: An empirical study of the Naive Bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, vol. 3, pp. 41–46 (2001)

    Google Scholar 

  10. le Vrang, M., Papantoniou, A., Pauwels, E., Fannes, P., Vandensteen, D., De Smedt, J.: ESCO: Boosting job matching in Europe with semantic interoperability. Computer 47(10), 57–64 (2014)

    Article  Google Scholar 

  11. Xie, J., Szymanski, B.K., Liu, X.: SLPA: uncovering overlapping communities in social networks via a speaker-listener interaction dynamic process. In: 2011 IEEE 11th International Conference on Data Mining Workshops, pp. 344–349. IEEE (2011)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Ecole Nationale Supérieure d’Informatique, BP 68M, 16309, Oued-Smar, Algiers, Algeria

    Sabrina Boudjedar, Sihem Bouhenniche & Hakim Mokeddem

  2. Laboratoire de Recherche En Intelligence Articifielle (LRIA), USTHB, El Alia BP 32, Bab Ezzouar, Algiers, Algeria

    Hamid Benachour

Authors
  1. Sabrina Boudjedar
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  2. Sihem Bouhenniche
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  3. Hakim Mokeddem
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  4. Hamid Benachour
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Corresponding author

Correspondence to Hakim Mokeddem .

Editor information

Editors and Affiliations

  1. International Hellenic University, Thessaloniki, Greece

    Emmanouel Garoufallou

  2. Complutense University of Madrid, Madrid, Spain

    María-Antonia Ovalle-Perandones

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Boudjedar, S., Bouhenniche, S., Mokeddem, H., Benachour, H. (2021). Automatic Human Resources Ontology Generation from the Data of an E-Recruitment Platform. In: Garoufallou, E., Ovalle-Perandones, MA. (eds) Metadata and Semantic Research. MTSR 2020. Communications in Computer and Information Science, vol 1355. Springer, Cham. https://doi.org/10.1007/978-3-030-71903-6_10

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  • DOI: https://doi.org/10.1007/978-3-030-71903-6_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71902-9

  • Online ISBN: 978-3-030-71903-6

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