An Explainable Resume Analyzer Using ESCO Ontology and Semantic Skill Mapping for Transparent Candidate Ranking
Sushma Malik, Anamika Rana, Ayush Pal, Ritham Gupta
Department of Computer Applications, Maharaja Surajmal Institute, New Delhi, India
Abstract: Traditional recruitment processes often rely heavily on manual resume screening, which is both time-consuming and prone to human bias. These challenges can lead to inefficiencies, inconsistent evaluations, and potential overlooking of qualified candidates. This study presents a locally deployed, desktop-based Resume Analyzer designed to automate the initial screening phase while ensuring enhanced data privacy for small and medium-sized organizations. Unlike cloud-based Applicant Tracking Systems (ATS), the proposed system operates entirely on a local machine, reducing the risk of sensitive applicant data exposure. The system utilizes Natural Language Processing (NLP) techniques to extract and analyze textual content from resumes in PDF, DOCX, and TXT formats. A specialized algorithm is implemented to accurately calculate total work experience by interpreting employment durations and aggregating them systematically. To standardize skill identification and improve matching accuracy, the system integrates a mapping mechanism based on the ESCO (European Skills, Competences, Qualifications and Occupations) framework developed by the European Commission. This ontology-driven approach ensures alignment with recognized occupational and skills classifications, promoting consistency and transparency in candidate evaluation. Candidates are ranked using a configurable weighted scoring model that evaluates three primary components: skills (50%), work experience (30%), and education (20%). The scoring system provides clear justifications for rankings, enhancing interpretability and recruiter trust. Experimental evaluation demonstrates that the system effectively identifies top candidates while maintaining fairness and transparency. The findings suggest that localized, ontology-based resume analysis tools can serve as a practical, cost-effective alternative to cloud-hosted ATS solutions, particularly in privacy-sensitive environments where data security and control are critical considerations.
Keywords: Resume Analysis, ESCO Ontology, Semantic Skill Mapping, Candidate Ranking, Explainable AI (XAI), Natural Language Processing (NLP), Skills Classification
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