Application of Named Entity Recognition (NER) in Job Vacancy Matching Using an Ontology-Based Approach (Case Study: Information Technology Sector)

Abstract

The dissemination of job vacancies through online platforms still faces limitations in understanding the semantic relationships between the skills possessed by job seekers and the qualifications required by a job position. This mismatch results in an inefficient search process and longer search times. This study aims to develop a semantic-based job vacancy recommendation application (talent matching) using a skill ontology approach. One of the main challenges in developing the ontology is the lack of standardized data structures in job vacancy postings, particularly in the job description section. To address this issue, Named Entity Recognition (NER) techniques are applied to automatically extract skill entities from job description texts. The extracted results are then classified into a taxonomy structure using SkillsGPT, thereby forming a hierarchical skill concept model semantically represented within the ontology using Protégé. The matching process between user skills and job qualifications is conducted through semantic similarity calculations employing the Sánchez Similarity method. Job vacancy data are collected via web scraping, while system development follows the Rational Unified Process (RUP) methodology and is evaluated using Black Box testing. Evaluation results demonstrate that the developed system is capable of providing semantically relevant job vacancy recommendations according to the user's skill profile. Therefore, this study contributes both theoretically and practically to the development of ontology-based recommendation systems, particularly in the automated modeling of skill taxonomies from unstructured data.