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Due to the rapid development of job markets, traditional recruitment methods are becoming insufficient. This is because employers often receive an enormous number of applications (usually unstructured resumes) that are difficult to process and analyze manually. To address this issue, several automatic recruitment systems have been proposed. Although these systems have proved to be more effective in processing candidate resumes and matching them to their relevant job posts, they still suffer from low precision due to limitations of their underlying techniques. On the one hand, approaches based on keyword matching ignore the semantics of the job post and resume contents, and consequently a large portion of the matching results is irrelevant. On the other hand, the more recent semantics-based models are influenced by the limitations of the used semantic resources, namely the incompleteness of the knowledge captured by such resources and their limited domain coverage. In this paper, we propose an automatic online recruitment system that employs multiple semantic resources to highlight the semantic contents of resumes and job posts. Additionally, it utilizes statistical concept-relatedness measures to further enrich the highlighted contents with relevant concepts that were not initially recognized by the used semantic resources. The proposed system has been instantiated and validated in a precision-recall based empirical framework.
Kmail et al. (Sun,) studied this question.