A Matching Approach Based on Term Clusters for eRecruitment
- 1.2k Downloads
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.
KeywordsCosine Similarity Term Cluster Pattern Rule Free Format Text Domain Consensus
This study is supported by TÜBİTAK TEYDEB programme with the project number 3130841.
- 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
- 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
- 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 2007Google Scholar