Papers by Adarsh Shrivastava
—Due to the rapid development of job markets, traditional recruitment methods are becoming insuff... more —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.
While Internet takes up by far the most significant part of our daily lives, finding jobs/employe... more While Internet takes up by far the most significant part of our daily lives, finding jobs/employees on the Internet has started to play a crucial role for job seekers and employers. Online recruitment websites and human resources consultancy and recruitment companies enable job seekers to create their résumé, a brief written formal document including job seeker's basic information such as personal information, educational information, work experience and qualifications in order to find and apply for desirable jobs, whereas they enable companies to find qualified employees they are looking for. However résumés may be written in many ways that make it difficult for online recruitment companies to keep these data in their relational databases. In this study, a project that Kariyer.net (largest online recruitment website in Turkey) and TUBITAK (The Scientific and Technological Research Council of Turkey) have been jointly working is proposed. In this mentioned project, a system enables free structured format of résumés to transform into an ontological structure model. The produced system based on ontological structure model and called Ontology based Résumé Parser (ORP) will be tested on a number of Turkish and English résumés. The proposed system will be kept in Semantic Web approach that provides companies to find expert finding in an efficient way.
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Papers by Adarsh Shrivastava