Determination of Professional Competencies Using an Alignment Algorithm of Academic Profiles and Job Advertisements, Based on Competence Thesauri and Similarity Measures

A Correction to this article was published on 13 January 2020

This article has been updated

Abstract

Describing the competencies required by a profession is essential for aligning online profiles of job seekers and job advertisements. Comparing the competencies described within each context has typically not be done, which has generated a complete disconnect in language between them. This work presents an approach for the alignment of online profiles and job advertisements, according to knowledge and skills, using measures of lexical, syntactic and taxonomic similarity. In addition, we use a ranking that allows the alignment of the profiles to the topics of a thesaurus that define competencies. The results are promising, because the combination of the measures of similarity with the alignment with thesauri of competencies offers robustness to the process of generation of professional competence descriptions. This combination allows dealing with the common problems of synonymy, homonymy, hypernymy/hyponymy and meronymy of the terms in Spanish. This research uses natural language processing to offer a novel approach for assessing the match of the competencies described by the applicants and by the employers, even if they use different terminology. The resulting approach, while developed in Spanish for computer science jobs, can be extended to other languages and domains, such is the case of recruitment, where it will contribute to the creation of better tools that give feedback to job seekers about how to best align their competencies with job opportunities.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Data Availability

The data is available in https://goo.gl/Q9d8Hx.

Change history

  • 13 January 2020

    In this issue, the citation information on the opening page of each article HTML was updated to read ���International Journal of Artificial Intelligence in Education December 2019���,��� not ���International Journal of Artificial Intelligence in Education December 2000...���

Notes

  1. 1.

    DISCO II, available online in http://disco-tools.eu/disco2_portal/projectInformation.php

  2. 2.

    Text Retrieval Conference, disponible en http://trec.nist.gov/

  3. 3.

    CONLL Format, VMN: verb, CC: conjunction.

References

  1. Aguilar, J., Valdiviezo, P., Cordero, J., Sánchez, M. (2015). Conceptual design of a smart classroom based on multiagent systems. In Proceedings of Int. Conf. Artificial Intelligence (471–477).

  2. Aguilar, J., Valdiviezo, P., & Riofrio, G. (2017). A general framework for intelligent recommender systems. Applied Computing and Informatics, Elsevier, 13(2), 147–160.

    Article  Google Scholar 

  3. Alqadah, F., & Bhatnagar, R. (2011). Similarity measures in formal concept analysis. Annals of Mathematics and Artificial Intelligence, 61(3), 245–256.

    MathSciNet  Article  Google Scholar 

  4. Anderson, L. W., Krathwohl, D. R. and Bloom, B. S. (2001). “A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives”, Allyn & Bacon.

  5. Beckers, J. (2011). “Développer et évaluer des compétences à l’école: vers plus d’efficacité et d’équité”. [Online]. Available: http://orbi.ulg.be/handle/2268/125331.

  6. Blanco-González, J., Ortega-González, Y., et al. (2011). Ontological models for professional competences management. Ingeniería Industrial, 32(3), 224–230.

    Google Scholar 

  7. Dane, M. (2012). System and method for automatically processing candidate resumes and job specifications expressed in natural language into a normalized form using frequency analysis. U.S. Patent 8,117,024, issued February 14.

  8. De Leenheer, P., Christiaens, S., & Meersman, R. (2010). Business semantics management: A case study for competency-centric HRM. Computers in Industry, 61(8), 760–775.

    Article  Google Scholar 

  9. Dijkman, R., Dumas, M., Van Dongen, B., Käärik, R., & Mendling, J. (2011). Similarity of business process models: Metrics and evaluation. Information Systems, 36(2), 498–516.

    Article  Google Scholar 

  10. Ehrig, M., Koschmider, A. and Oberweis, A. (2007). Measuring similarity between semantic business process models. Fourth Asia-Pacific conference on Conceptual Modelling, Australian Computer Society, Inc., pp. 71–80.

  11. Faria, C., Serra, I., & Girardi, R. (2014). A domain-independent process for automatic ontology population from text. Science of Computer Programming, 95, 26–43.

    Article  Google Scholar 

  12. Fazel-Zarandi, M. (2013). Representing and reasoning about skills and competencies over time. Ph.D. dissertation, Toronto Univ., Canada.

  13. Gluga, R., Kay, J., & Lever, T. (2013). Foundations for modeling university curricula in terms of multiple learning goal sets. IEEE Transactions on Learning Technologies, 6, 25–37.

    Article  Google Scholar 

  14. Gomaa, W. H. and Fahmy, A. A. (2013). A survey of text similarity approaches. International Journal of Computer Applications.

  15. González-Eras, A. (2017). Caracterización de las competencias en los contextos laboral y académico en base a tecnologías semánticas. M.S. thesis. E.T.S.I.S.I., Universidad Politécnica de Madrid, Madrid, España.

  16. González-Eras, A., & Aguilar, J. (2015). Semantic Architecture for the Analysis of the Academic and Occupational Profiles Based on Competencies. Contemporary Engineering Sciences, 8(33), 1551–1563.

    Article  Google Scholar 

  17. González-Eras, A., Aguilar, J. (2019). Esquema para la actualización de Ontologías de Competencias en base al Procesamiento del Lenguaje Natural y la Minería Semántica. Revista Ibérica de Sistemas e Tecnologias de Informação, E17, (pp. 433–447).

  18. González-Eras, A., Buendia, O., Aguilar, J., Cordero, J., Rodriguez, T. (2017). Competences as services in the autonomic cycles of learning analytic tasks for a smart classroom. In Technologies and Innovation (R. Valencia-García, et al., Eds.), Communications in Computer and Information Science Series, Vol. 749, Springer, pp. 211-226.

  19. Guevara, C., Gonzalez, A., & Aguilar, J. (2017). The model of adaptive learning objects for virtual environments instanced by the competencies. Advances in Science, Technology and Engineering Systems Journal, 2(3), 345–355.

    Article  Google Scholar 

  20. Harispe, S., Ranwez, S., Janaqi, S. and Montmain, J. (2013). Semantic measures for the comparison of units of language, concepts or instances, Parc scientifique, France: LGI2P/EMA Research Center.

  21. Jones, K., Walker, S., & Robertson, S. (2000). A probabilistic model of information retrieval: development and comparative experiments. Information Processing & Management, 36, 809–840.

    Article  Google Scholar 

  22. Kalmukov, Y. (2013). Describing papers and reviewers’ competences by taxonomy of keywords. arXiv preprint.

  23. Levenshtein, V. I. (1966). Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady, 10(8), 707–710.

    MathSciNet  Google Scholar 

  24. Lundqvist, K., Baker, K., & Williams, S. (2011). Ontology supported competency system. International Journal of Knowledge and Learning, 7(3–4), 197–219.

    Article  Google Scholar 

  25. Malzahn, N., Ziebarth, S., & Hoppe, H. (2013). Semi-automatic creation and exploitation of competence ontologies for trend aware profiling, matching and planning. Knowledge Management & E-Learning: An International Journal (KM&EL), 5(1), 84–103.

    Google Scholar 

  26. Manning, C. D., Raghavan, P., and Schütze, H. (2009). An introduction to information retrieval. Cambridge University Press.

  27. Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., McClosky, D. (2014). The Stanford CoreNLP natural language processing toolkit, In Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations, (pp. 55–60).

  28. Mendonza, M., Perozo, N. and Aguilar, J. (2015). An approach for Multiple Combination of Ontologies based on the Ants Colony Optimization Algorithm. Asia-Pacific Conference on Computer Aided System Engineering, Quito: IEEE, Ecuador, pp. 140–145.

  29. Montuschi, P., Lamberti, F., Gatteschi, V. and Demartini, C. (2015) A semantic recommender system for adaptive learning. IT Professional, 50–58.

    Article  Google Scholar 

  30. Müller-Riedlhuber, H. (2009) The European dictionary of skills and competencies (DISCO): an Example of Usage Scenarios for Ontologies. I-SEMANTICS, pp. 467–479.

  31. Müller-Riedlhuber, H. (2017). DISCO II the European dictionary of skills and competences. [Online]. Available: http://disco-tools.eu/disco2_portal/projectInformation.php

  32. Nishioka, C., Große-Bölting, G. and Scherp, A. (2015). Influence of time on user profiling and recommending researchers in social media, 15th International Conference on Knowledge Technologies and Data-driven Business, ACM, pp. 9.

  33. Ortiz Sánchez, C. L. (2016). Verificación de competencias académicas en base a niveles de habilidad mediante elementos semánticos, thesis, Dept. Computer Sciences and Electronics, Universidad Técnica Particular de Loja, Loja.

  34. Paquette, G. (2007). An ontology and a software framework for competency modeling and management. Educational Technology & Society, 10(3), 1–21.

    Google Scholar 

  35. Paquette, G. (2016). Competency-based personalization process for smart learning environments. Learning, Design, and Technology. International Compendium of Theory, Research, Practice, and Policy, pp. 20–36.

  36. Paquette, G., Rogozan, D. and Marino, O. (2012). Competency comparison relations for recommendation in technology enhanced learning scenarios, CEUR Workshop.

  37. Pawełoszek, I. (2017). Ontological support for process-oriented competency management. In Information Technology for Management. Ongoing Research and Development, pp. 41–60.

  38. Rácz, G., Sali, A., Schewe, K. D. (2018). Refining semantic matching for job recruitment: An application of formal concept analysis. In International Symposium on Foundations of Information and Knowledge Systems, pp. 322–339.

    Google Scholar 

  39. Rau, M. (2017). Do knowledge-component models need to incorporate representational competencies? International Journal of Artificial Intelligence in Education, 27, 298–319.

    Article  Google Scholar 

  40. Reichhold, M., Kerschbaumer, J., Fliedl, G. and Winkler, C. (2012). Automatic generation of user role profiles for optimizing enterprise search. In 24th International Conference on Software & Systems Engineering and their applications, vol. 24, pp 241–248.

  41. Robertson, S., & Zaragoza, H. (2009). The probabilistic relevance framework: BM25 and beyond. Foundations and Trends in Information Retrieval, 3(4), 333–389.

    Article  Google Scholar 

  42. Rosa, J., Kich, M., & Brito, L. (2015). A multi-temporal context-aware system for competences management. International Journal of Artificial Intelligence in Education, 25, 455–492.

    Article  Google Scholar 

  43. M. Sánchez, J. Aguilar, J. Cordero, P. Valdiviezo (2015).Basic features of a reflective middleware for intelligent learning environment in the cloud (IECL). Asia-Pacific Conference on Computer Aided System Engineering.

  44. Sanchez, M., Cordero, J., Valdiviezo, P., Barba, L., & Chamba, L. (2018). Learning analytics tasks as services in smart classroom. Universal Access in the Information Society Journal, Springer, 17(4), 693–709.

    Article  Google Scholar 

  45. Sateli, B., Löffler, F., König-Ries, B., Witte, R. (2017). ScholarLens: extracting competences from research publications for the automatic generation of semantic user profiles. 3, Peer J Computer Science.

  46. Smirnov, A., Kashevnik, A., Balandin, S., Baraniuc, O., Parfenov, V. (2016). Competency management system for technopark residents: Smart space-based approach. In Internet of Things, Smart Spaces, and Next Generation Networks and Systems (pp. 15–24). Springer, Cham.

    Google Scholar 

  47. Van Dongen, B., Dijkman, R., & Mendling, J. (2013). Measuring similarity between business process models. Seminal Contributions to Information Systems Engineering, 405–419.

  48. Weren, E., Kauer, A. U., Mizusaki, L., Moreira, V. P., de Oliveira, J. and Wives, L. K. (2014). Examining multiple features for author profiling. Journal of Information and Data Management, pp. 266.

  49. Worsley, M., & Blikstein, P. (2018). A multimodal analysis of making. International Journal of Artificial Intelligence in Education, 28, 385–419.

    Article  Google Scholar 

  50. Yuanhua, L., & Zhailk, C. (2011). Lower-bounding term frequency normalization. In Proc. 20th ACM international conference on Information and knowledge management, pp. 7–16.

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Alexandra González-Eras.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

González-Eras, A., Aguilar, J. Determination of Professional Competencies Using an Alignment Algorithm of Academic Profiles and Job Advertisements, Based on Competence Thesauri and Similarity Measures. Int J Artif Intell Educ 29, 536–567 (2019). https://doi.org/10.1007/s40593-019-00185-z

Download citation

Keywords

  • Professional competencies
  • Academic profiles
  • Job advertisements
  • Competence thesauri
  • Similarity measures
  • Alignment of profiles