Acta Informatica Pragensia 2023, 12(2), 200-224 | DOI: 10.18267/j.aip.2043770

AnnoJOB: Semantic Annotation-Based System for Job Recommendation

Assia Brek ORCID..., Zizette Boufaida
LIRE Laboratory, Software and Information Systems Technologies Department, Faculty of Information and Communication Technology, Constantine 2 University – Abdelhamid Mehri, Constantine, Algeria

With the vast success of e-recruitment, online job offers have increased. Therefore, there is a number of job portals and recommendation systems trying to help users filter this massive amount of offers when searching for the right job. Until today, most of these systems' searching techniques are confined to using keywords such as job titles or skills, which also returns many results. This paper proposes a job recommender system that exploits the candidate's resume to select the appropriate job. Our system, AnnoJob, adopts a semantic annotation approach to: (1) intelligently extract contextual entities from resumes/offers, and (2) semantically structure the extracted entities in RDF triples using domain ontology, providing a unified presentation of the content of the documents. Furthermore, to select the suitable offer, we propose a novel semantic matching technique that computes the similarity between the resume/offers based on identifying the semantic similarity and relatedness between the RDF triples using the domain ontology and Wikidata, which enhance job-ranking results over existing information retrieval approaches. We evaluate our system using various experiments on data from real-world recruitment documents.

Keywords: Information extraction; Semantic matching; Ontology; Relatedness; Semantic similarity; Knowledge graphs.

Received: August 15, 2022; Revised: December 27, 2022; Accepted: December 30, 2022; Prepublished online: January 17, 2023; Published: October 10, 2023  Show citation

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Brek, A., & Boufaida, Z. (2023). AnnoJOB: Semantic Annotation-Based System for Job Recommendation. Acta Informatica Pragensia, 12(2), 200-224. doi: 10.18267/j.aip.204
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