Ontology-based skill matching algorithms
| Autor: | Ciprian Stan, Marcel Antal, Tudor Cioara, Ionut Anghel, Teodor Petrican, Ioan Salomie |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
060201 languages & linguistics
ComputingMilieux_THECOMPUTINGPROFESSION Java Computer science business.industry Ontology-based data integration 06 humanities and the arts 02 engineering and technology Ontology (information science) Machine learning computer.software_genre 0602 languages and literature 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Algorithm design Artificial intelligence business computer Algorithm Ontology alignment computer.programming_language |
| Zdroj: | ICCP |
| DOI: | 10.1109/iccp.2017.8117005 |
| Popis: | Automatic recommendations based on skill matching techniques can prove to be an important component of an online recruitment platform, being able to lower the costs for employers, ease the process for candidates and increase the hiring quality overall. This is important nowadays, when online recruitment plays a major role in the hiring process. The main challenges in this area consist in providing relevant and computationally inexpensive results. In this paper we propose a semantic approach to the skill matching problem in the context of online recruitment. We present a metric of similarity based on a skills ontology and three algorithms on top of this metric, with the intent of obtaining a ranked skill-based matching between candidates and job offers. We also provide an analysis of those algorithms in terms of advantages, disadvantages and complexity. |
| Databáze: | OpenAIRE |
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