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A Matching Approach Based on Term Clusters for eRecruitment

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 9728)


As the Internet occupies our daily lives in all aspects, finding jobs/employees online has an important role for job seekers and companies that hire. However, it is difficult for a job applicant to find the best job that matches his/her qualifications and also it is difficult for a company to find the best qualified candidates based on the company’s job advertisement. In this paper, we propose a system that extracts data from free-structured job advertisements in an ontological way in Turkish language. We describe a system that extracts data from resumés and jobs to generate a matching system that provides job applicants with the best jobs to match their qualifications. Moreover, the system also provides companies to find the best fit for their job advertisement.


  • Cosine Similarity
  • Term Cluster
  • Pattern Rule
  • Free Format Text
  • Domain Consensus

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  • DOI: 10.1007/978-3-319-41561-1_29
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  1. Crow, D., DeSanto, J.: A hybrid approach to concept extraction and recognition-based matching in the domain of human resources. In: Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI). IEEE (2004)

    Google Scholar 

  2. Mochol, M., Wache, H., Nixon, L.J.: Improving the accuracy of job search with semantic techniques. In: Abramowicz, W. (ed.) BIS 2007. LNCS, vol. 4439, pp. 301–313. Springer, Heidelberg (2007)

    CrossRef  Google Scholar 

  3. Colucci, S., Di Noia, T., Di Sciascio, E., Donini, F.M., Mongiello, M., ve Mottola, M.: A formal approach to ontology-based semantic match of skills descriptions. J. Univ. Comput. Sci. 9(12), 1437–1454 (2003)

    Google Scholar 

  4. Mochol, M., Paslaru, E., ve Simperl, B.: Practical guidelines for building semantic eRecruitment applications. In: Proceedings of International Conference on Knowledge Management, Special Track: Advanced Semantic Technologies (AST) (2006). Gonzàlez, E., Fuentes, M.: A new lexical chain algorithm used for automatic summarization. In: Proceedings of the 12th International Congress of the Catalan Association of Artificial Intelligence (CCIA) (2009)

    Google Scholar 

  5. Le, B.T., Dieng-Kuntz, R., Ve Gandon, F.: On ontology matching problems for building a corporate semantic web in a multi-communities organization. In: Proceedings of the Sixth International Conference on Enterprise Information Systems, Kluwer, Porto (2005)

    Google Scholar 

  6. Ehrig, M., Sure, Y.: Ontology mapping - an integrated approach. In: Bussler, C.J., Davies, J., Fensel, D., Studer, R. (eds.) ESWS 2004. LNCS, vol. 3053, pp. 76–91. Springer, Heidelberg (2004)

    CrossRef  Google Scholar 

  7. Hassan, F., Ghani, I., Faheem, M., Hajji, A.: Ontology matching approaches for eRecruitment. In: Proceedings of ESWS, LNCS, vol. 3053, pp. 76–91. Springer (2004). International Journal of Computer Applications (2012)

    Google Scholar 

  8. Navigli, R., Velardi, P.: Learning domain ontologies from document warehouses and dedicated web sites. Association of the Computational Linguistics (2004)

    Google Scholar 

  9. Sclano, F., Velardi, P.: Term extractor: a web application to learn the common terminology of interest groups and research communities. In: TIA 2007

    Google Scholar 

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This study is supported by TÜBİTAK TEYDEB programme with the project number 3130841.

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Correspondence to Kemal Can Kara .

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© 2016 Springer International Publishing Switzerland

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Bal, G., Karakaş, A., Güngör, T., Süzen, F., Kara, K.C. (2016). A Matching Approach Based on Term Clusters for eRecruitment. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2016. Lecture Notes in Computer Science(), vol 9728. Springer, Cham.

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  • Print ISBN: 978-3-319-41560-4

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