Abstract
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.
Keywords
- Cosine Similarity
- Term Cluster
- Pattern Rule
- Free Format Text
- Domain Consensus
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Acknowledgements
This study is supported by TÜBİTAK TEYDEB programme with the project number 3130841.
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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. https://doi.org/10.1007/978-3-319-41561-1_29
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DOI: https://doi.org/10.1007/978-3-319-41561-1_29
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