Semantic Interpretation in Job Recommendations: Ontology-Driven Resume Parsing for Personalized Job Recommendation Systems

Duygu Çelik  Ertugrul; Selin Bitirim

doi:10.4025/actascitechnol.v47i1.72998

Autores

Duygu Çelik Ertugrul Eastern Mediterranean University
Selin Bitirim Eastern Mediterrenean University

DOI:

https://doi.org/10.4025/actascitechnol.v47i1.72998

Palavras-chave:

Job recommendation systems; ontology; text parsing; information extraction; semantic web.

Resumo

In this study, an ontology-based Hybrid Job Recommendation System (HJRS) has been proposed to facilitate job seekers in finding the right positions and employers in identifying the most suitable candidates in a complex and dynamic job market. This system has been developed to better meet the needs inadequately addressed by traditional job recommendation systems (JRS), by combining both syntactic and semantic approaches. HJRS consists of three main components: (1) Resume Recommender System Ontology—RRSO, (2) Ontology-Based Resume Text Parsing Module (OntRTPM), and (3) System Database. While RRSO is used to semantically interpret job postings and candidate information, OntRTPM extracts the necessary personal information, educational background, work experiences, and skills from candidates' resumes. The candidate extracted data is saved in the appropriate field of the system database, and an OWL file is created for the candidate. The system operates through four main steps: (1) preprocessing, (2) feature processing, (3) ontology matching and labelling, and (4) saving structured resume data. In the preprocessing stage, data is cleaned and normalized; in the feature processing stage, concept extraction and semantic matching operations are performed. As a result of these processes, candidates' resumes are stored in both OWL file and the relevant tables in the system database. The dataset used consists of 100 anonymized Turkish resumes randomly selected from the database of Kariyer.Net, a large career company in Turkey. OntRTPM has been subjected to accuracy and reliability tests by extracting information from these resumes. The system aims to significantly improve job search and recruitment processes by providing more accurate and effective recommendations for both job seekers and employers in the job market. Future work will include expanding the resume dataset and further optimizing system performance.

Downloads

Não há dados estatísticos.

Referências

Ak?n, A. A., & Ak?n, M. D. (2007). Zemberek, an open-source NLP framework for Turkic languages. Structure, 10(2007), 1-5. https://doi.org/10.47769/izufbed.880143

Bafna, P., Shirwaikar, S., & Pramod, D. (2019). Task recommender system using semantic clustering to identify the right personnel. VINE Journal of Information and Knowledge Management Systems, 49(2), 181-199. https://doi.org/10.1108/VJIKMS-08-2018-0068

Batbaatar, E., & Ryu, K. H. (2019). Ontology-based healthcare named entity recognition from Twitter messages using a recurrent neural network approach. International Journal of Environmental Research and Public Health, 16(19), 3628. https://doi.org/10.3390/ijerph16193628

BIOES. (2024). GeeksforGeeks. from https://www.geeksforgeeks.org/nlp-iob-tags/

Bitirim, S., & Ertu?rul, D. Ç. (2024). ?nsan Kaynaklarinda Etkili ??e Alim Süreci ?çin Türkçe Bir Ontoloji Geli?tirilmesi (Development of a Turkish Ontology for Effective Recruitment Process in Human Resources—RRSO: Resume Recommender System Ontology). Kahramanmara? Sütçü ?mam Üniversitesi Mühendislik Bilimleri Dergisi, 27(2), 401-414. https://doi.org/10.17780/ksujes.1390172

Bitirim, Y., Bitirim, S., Ertugrul, D. C., & Toygar, O. (2020, July). An evaluation of reverse image search performance of Google. In 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC) (pp. 1368-1372). IEEE.

Celik, D. (2016). Towards a semantic-based information extraction system for matching résumés to job openings. Turkish Journal of Electrical Engineering and Computer Sciences, 24(1), 141-159. https://doi.org/10.3906/elk-1304-130

Celik, D., & Elçi, A. (2005a). A semantic search agent approach: finding appropriate semantic Web services based on user request term (s). In 2005 International Conference on Information and Communication Technology (pp. 675-687). IEEE.

Çelik, D., & Elçi, A. (2005b). Searching semantic Web services: An intelligent agent approach using semantic enhancement of client input term (s) and matchmaking step. In International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06) (Vol. 2, pp. 916-922). IEEE.

Celik, D., & Elci, A. (2008). Provision of semantic Web services through an intelligent semantic Web service finder. Multiagent and Grid Systems, 4(3), 315-334.

Çelik, D., & Elçi, A. (2011). Ontology-based matchmaking and composition of business processes. In Semantic Agent Systems: Foundations and Applications (pp. 133-157).

Çetinda?, C., Yaz?c?o?lu, B., & Koç, A. (2023). Named-entity recognition in Turkish legal texts. Natural Language Engineering, 29(3), 615-642. https://doi.org/10.1017/S1351324922000304

Chandak, A. V., Pandey, H., Rushiya, G., & Sharma, H. (2024). Resume parser and job recommendation system using machine learning. In 2024 International Conference on Emerging Systems and Intelligent Computing (ESIC) (pp. 157-162). IEEE.

Das, P., Pandey, M., & Rautaray, S. S. (2018). A CV parser model using entity extraction process and big data tools. IJ Information Technology and Computer Science, 9, 21-31. https://doi.org/10.5815/ijitcs.2018.09.03

Deepak, G., Teja, V., & Santhanavijayan, A. (2020). A novel firefly driven scheme for resume parsing and matching based on entity linking paradigm. Journal of Discrete Mathematical Sciences and Cryptography, 23(1), 157-165. https://doi.org/10.1080/09720529.2020.1721879

Ertu?rul, D. Ç., & Bitirim, S. (2025). Job recommender systems: A systematic literature review, applications, open issues, and challenges. Journal of Big Data, 12. https://doi.org/10.1186/s40537-025-01173-y

Fernández-Reyes, F. C., & Shinde, S. (2019). CV retrieval system based on job description matching using hybrid word embeddings. Computer Speech & Language, 56, 73-79. https://doi.org/10.1016/j.csl.2019.01.003

Gaur, B., Saluja, G. S., Sivakumar, H. B., & Singh, S. (2021). Semi-supervised deep learning based named entity recognition model to parse education section of resumes. Neural Computing and Applications, 33, 5705-5718. https://doi.org/10.1007/s00521-020-05351-2

Gugnani, A., & Misra, H. (2020). Implicit skills extraction using document embedding and its use in job recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 08, pp. 13286-13293). https://doi.org/10.1609/aaai.v34i08.7038

Jiechieu, K. F. F., & Tsopze, N. (2021). Skills prediction based on multi-label resume classification using CNN with model predictions explanation. Neural Computing and Applications, 33(10), 5069-5087. https://doi.org/10.1007/s00521-020-05302-x

Lin, Y., Lei, H., Addo, P. C., & Li, X. (2016). Machine learned resume-job matching solution. arXiv preprint arXiv:1607.07657. https://doi.org/10.48550/arXiv.1607.07657

Mashayekhi, Y., Li, N., Kang, B., Lijffijt, J., & De Bie, T. (2024). A challenge-based survey of e-recruitment recommendation systems. ACM Computing Surveys, 56(10), 1-33.

Miller, G. A. (1995). WordNet: a lexical database for English. Communications of the ACM, 38(11), 39-41.

Min, H., Yang, B., Allen, D. G., Grandey, A. A., & Liu, M. (2024). Wisdom from the crowd: Can recommender systems predict employee turnover and its destinations? Personnel Psychology, 77(2), 475-496.

Mittal, V., Mehta, P., Relan, D., & Gabrani, G. (2020). Methodology for resume parsing and job domain prediction. Journal of Statistics and Management Systems, 23(7), 1265-1274. https://doi.org/10.1080/09720510.2020.1799583

Mughaid, A., Obeidat, I., Hawashin, B., AlZu'bi, S., & Aqel, D. (2019). A smart geo-location job recommender system based on social media posts. In 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS) (pp. 505-510). IEEE. https://doi.org/10.1109/SNAMS.2019.8931854

Natural Language Toolkit. (2024, July 19). https://www.nltk.org/

Nigam, A., Roy, A., Singh, H., & Waila, H. (2019). Job recommendation through progression of job selection. In 2019 IEEE 6th International Conference on Cloud Computing and Intelligence Systems (CCIS) (pp. 212-216). IEEE. https://doi.org/10.1109/CCIS48116.2019.9073723

O’Connor, M., Knublauch, H., Tu, S., Grosof, B., Dean, M., Grosso, W., & Musen, M. (2005). Supporting rule system interoperability on the semantic web with SWRL. In The Semantic Web–ISWC 2005: 4th International Semantic Web Conference, ISWC 2005, Galway, Ireland, November 6-10, 2005. Proceedings 4 (pp. 974-986). Springer Berlin Heidelberg.

Pawar, S., Thosar, D., Ramrakhiyani, N., Palshikar, G. K., Sinha, A., & Srivastava, R. (2021). Extraction of complex semantic relations from resumes. In ASEA workshop@ IJCAI.

Python Spyder. (2024, July 19). https://www.spyder-ide.org/

Qi, P., Zhang, Y., Zhang, Y., Bolton, J., & Manning, C. D. (2020). Stanza: A Python natural language processing toolkit for many human languages. arXiv preprint arXiv:2003.07082. https://doi.org/10.48550/arXiv.2003.07082

Qin, C., Zhu, H., Zhu, C., Xu, T., Zhuang, F., Ma, C., ... & Xiong, H. (2019). DuerQuiz: A personalized question recommender system for intelligent job interview. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2165-2173). https://doi.org/10.1145/3292500.3330706

Sandanayake, T. C., Limesha, G. A. I., Madhumali, T. S. S., Mihirani, W. P. I., & Peiris, M. S. A. (2018). Automated CV analyzing and ranking tool to select candidates for job positions. In Proceedings of the 6th International Conference on Information Technology: IoT and Smart City (pp. 13-18). https://doi.org/10.1145/3301551.3301579

Sang, E. F., & Veenstra, J. (1999). Representing text chunks. arXiv preprint cs/9907006.

Stanza Tool. (2024, July 19). https://stanfordnlp.github.io/stanza/

Tobing, B. C. L., Suhendra, I. R., & Halim, C. (2019). Catapa resume parser: End to end Indonesian resume extraction. In Proceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval (pp. 68-74). https://doi.org/10.1145/3342827.3342832

Wang, Y., Qin, J., & Wang, W. (2017). Efficient approximate entity matching using jaro-winkler distance. In International conference on web information systems engineering (pp. 231-239). Cham: Springer International Publishing.

Xu, L., Liu, J., & Gu, Y. (2018, July). A recommendation system based on extreme gradient boosting classifier. In 2018 10th International Conference on Modelling, Identification and Control (ICMIC) (pp. 1-5). IEEE. https://doi.org/10.1109/ICMIC.2018.8529885

Zou, Z., Huspi, S. H., & Nuar, A. N. A. (2024). A review on job recommendation system. Journal of Advanced Research in Applied Sciences and Engineering Technology, 41(2), 113-124.