Online Recruitment System based on Multiple Semantic Resources

The growth of online recruitment has spurred the need for more effective automated systems. On the one hand, traditional approaches based on keyword-based matching techniques suffer from low precision, i.e. a large fraction of the systems' suggestions are irrelevant. On the other hand, the newer semantics-based approaches are penalized by limitations of the exploited semantic resources, namely semantic knowledge incompleteness and limited domain coverage. In this paper, we present an automatic semantics-based online recruitment system that reuses knowledge captured in multiple existing semantic resources to match between candidate resumes and job posts. In addition, we use statistical-based concept-relatedness measures to alleviate the problem of semantic knowledge incompleteness in the exploited resources. An experimental instantiation of the proposed system has been installed to validate its effectiveness in matching job applicants to job posts.