Design and Research of Intelligent Screening System for Graduate Recruitment Based on Big Data Assisted Ontology-based Blockchain Design

Abstract

In recent years, several e-recruitment tools for recruiting job applicants have extended considerably. Companies regularly receive e-recruitment tools and a job portal from candidates who are qualified by post and a list of candidates manually. The existing mechanisms of e-recruitment are primarily used to store contact data for qualified candidates. This paper suggests a big data assisted ontology-based blockchain design (BDOBD) as an intelligent screening system for evaluating job candidates using ontological mapping. BDOBD consists of three steps of screening applicants for recruitment. The system collects and constructs the ontology document on candidates’ characteristics in the first step. The second step and third step of BDOBD map the job requirement ontology onto the candidate ontology document and finds the criteria for candidates Job openings/job requirements as ontology. In step 2, job openings/job requirements are shown as ontology. In step 3, BDOBD maps it onto applicant ontology documents the job requirements ontology and retrieves qualified applicants. Experimental results show that this model improves the accuracy of job requirements for competing applicants.