Enhancing Information Extraction Process in Job Recommendation using Semantic Technology

doi:10.23940/ijpe.22.05.p7.369379

Abstract

Abstract: Recently, the internet has become the first destination as a recruitment market, which has increased the number of job offers and resumes online. Recommendation systems are proposed to help users filter this massive amount of information, selecting the best candidate or the relevant offer. Processing the content of the documents correctly not only can reduce the matching complexity but also improve the recommender performance. This paper presents a semantic-based information extraction process, which intelligently and automatically extracts domain entities. The extracted entities are inter-linked to build domain context utilizing domain ontology covering the most significant and common parts of job offers/resumes. Moreover, the extracted information is structured in RDF triples delivering a semantic and unified presentation of documents data. The used ontology is dynamically enriched with both domain instances and relations to keep up with the constant change of the relevant data. We evaluate our system using various experiments on data from real-world recruitment documents. Our test results show that our approach can achieve a precision value of more than 90% in extracting domain-speciﬁc information.

Key words: information extraction, semantic technology, ontology, enrichment, semantic similarity

Assia Brek and Zizette Boufaida. Enhancing Information Extraction Process in Job Recommendation using Semantic Technology [J]. Int J Performability Eng, 2022, 18(5): 369-379.

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