Acta Informatica Pragensia 2023, 12(2), 200-224 | DOI: 10.18267/j.aip.2043901

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

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
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
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Ahmed Awan, M. N., Khan, S., Latif, K., & Khattak, A. M. (2019). A New Approach to Information Extraction in User-Centric E-Recruitment Systems. Applied Sciences, 9(14), Article no. 2852. https://doi.org/10.3390/app9142852 Go to original source...
    2. Balachander, Y., & Moh, T. S. (2018). Ontology based similarity for information technology skills. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 302-305). IEEE. https://doi.org/10.1109/ASONAM.2018.8508726 Go to original source...
    3. Bizer, C., Heese, R., Mochol, M., Oldakowski, R., Tolksdorf, R., & Eckstein, R. (2005). The impact of semantic web technologies on job recruitment processes. In Wirtschaftsinformatik 2005 (pp. 1367-1381). Physica. https://doi.org/10.1007/3-7908-1624-8_72 Go to original source...
    4. Bobadilla, J., Ortega, F. B., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge Based Systems, 46, 109-132. https://doi.org/10.1016/j.knosys.2013.03.012 Go to original source...
    5. Brek, A., & Boufaida, Z. (2020). Semantic Approaches Survey for Job Recommender Systems. In RIF'20: The 91th Seminary of Computer Science Research at Feminine. CEUR Workshop Proceedings. https://ceur-ws.org/Vol-3176/paper8.pdf
    6. Brek, A., & Boufaida, Z. (2022). Enhancing Information Extraction Process in Job Recommendation using Semantic Technology. International Journal of Performability Engineering, 18(5), 369-379. https://doi.org/10.23940/ijpe.22.05.p7.369379 Go to original source...
    7. Çelik, D., & Elçi, A. (2012). An ontology-based information extraction approach for résumés. In Joint international conference on pervasive computing and the networked world (pp. 165-179). Springer. https://doi.org/10.1007/978-3-642-37015-1_14 Go to original source...
    8. Chen, J., Zhang, J., & Niu, Z. (2018). A Two-Step Resume Information Extraction Algorithm. Mathematical Problems in Engineering, 2018, 1-8. https://doi.org/10.1155/2018/5761287 Go to original source...
    9. Ciravegna, F., & Lavelli, A. (2004). LearningPinocchio: adaptive information extraction for real world applications. Natural Language Engineering, 10(2), 145-165. https://doi.org/10.1017/s135132490400333x Go to original source...
    10. Daramola, J. O., Oladipupo, O. O., & Musa, A. G. (2010). A fuzzy expert system (FES) tool for online personnel recruitments. International Journal of Business Information Systems, 6(4), 444-462. Go to original source...
    11. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. https://arxiv.org/abs/1810.04805
    12. Dhameliya, J., & Desai, N. (2019). Job recommender systems: A survey. In 2019 innovations in power and advanced computing technologies (i-PACT) (Vol. 1, pp. 1-5). IEEE. https://doi.org/10.1109/i-PACT44901.2019.8960231 Go to original source...
    13. Färber, M., Bartscherer, F., Menne, C., & Rettinger, A. (2017). Linked data quality of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO. Semantic Web, 9(1), 77-129. https://doi.org/10.3233/sw-170275 Go to original source...
    14. Färber, M., & Rettinger, A. (2018). Which Knowledge Graph Is Best for Me?. arXiv preprint arXiv:1809.11099. https://doi.org/10.48550/arXiv.1809.11099 Go to original source...
    15. Guo, S., Alamudun, F., & Hammond, T. (2016). RésuMatcher: A personalized résumé-job matching system. Expert Systems with Applications, 60, 169-182. https://doi.org/10.1016/j.eswa.2016.04.013 Go to original source...
    16. Han, J., Zheng, L., Xu, Y., Zhang, B., Zhuang, F., Philip, S. Y., & Zuo, W. (2019). Adaptive deep modeling of users and items using side information for recommendation. IEEE transactions on neural networks and learning systems, 31(3), 737-748. https://doi.org/10.1109/TNNLS.2019.2909432 Go to original source...
    17. Heggo, I. A., & Abdelbaki, N. (2018). Hybrid information filtering engine for personalized job recommender system. In International Conference on Advanced Machine Learning Technologies and Applications (pp. 553-563). Springer. https://doi.org/10.1007/978-3-319-74690-6_54 Go to original source...
    18. Karaa, W. B. A., & Mhimdi, N. (2011). Using ontology for resume annotation. International Journal of Metadata, Semantics and Ontologies, 6(3/4), 166-174. https://doi.org/10.1504/ijmso.2011.048018 Go to original source...
    19. Kumaran, V. S., & Sankar, A. (2013). Towards an automated system for intelligent screening of candidates for recruitment using ontology mapping (EXPERT). International Journal of Metadata, Semantics and Ontologies, 8(1), 56. https://doi.org/10.1504/ijmso.2013.054184 Go to original source...
    20. Lavi, D., Medentsiy, V., & Graus, D. (2021). consultantbert: Fine-tuned siamese sentence-bert for matching jobs and job seekers. arXiv preprint arXiv:2109.06501. https://doi.org/10.48550/arXiv.2109.06501 Go to original source...
    21. Fellbaum, C., & Miller, G. (1998). Combining local context and WordNet similarity for word sense identification. In WordNet: An electronic lexical database (265-283). MIT Press. Go to original source...
    22. Lu, Y., El Helou, S., & Gillet, D. (2013). A recommender system for job seeking and recruiting website. In Proceedings of the 22nd International Conference on World Wide Web (pp. 963-966). ACM. https://doi.org/10.1145/2487788.2488092 Go to original source...
    23. Manning, C. D. (2008). Introduction to information retrieval. Syngress Publishing. Go to original source...
    24. Maree, M., Kmail, A. B., & Belkhatir, M. (2019). Analysis and shortcomings of e-recruitment systems: Towards a semantics-based approach addressing knowledge incompleteness and limited domain coverage. Journal of Information Science, 45(6), 713-735. https://doi.org/10.1177/0165551518811449 Go to original source...
    25. Martinez-Gil, J., Paoletti, A. L., & Schewe, K. D. (2016). A smart approach for matching, learning and querying information from the human resources domain. In East European Conference on Advances in Databases and Information Systems (pp. 157-167). Springer. https://doi.org/10.1007/978-3-319-44066-8_17 Go to original source...
    26. Martínez-Rodríguez, J., Hogan, A., & Lopez-Arevalo, I. (2020). Information extraction meets the Semantic Web: A survey. Semantic Web, 11(2), 255-335. https://doi.org/10.3233/sw-180333 Go to original source...
    27. Mishra, R., & Rathi, S. (2021). Enhanced DSSM (deep semantic structure modelling) technique for job recommendation. Journal of King Saud University - Computer and Information Sciences, 34(9), 7790-7802. https://doi.org/10.1016/j.jksuci.2021.07.018 Go to original source...
    28. Mochol, M., Wache, H., & Nixon, L. (2007). Improving the accuracy of job search with semantic techniques. In International Conference on Business Information Systems (pp. 301-313). Springer. https://doi.org/10.1007/978-3-540-72035-5_23 Go to original source...
    29. Rácz, G., Sali, A., & Schewe, K. D. (2016). Semantic matching strategies for job recruitment: A comparison of new and known approaches. In Foundations of Information and Knowledge Systems (pp. 149-168). Springer. https://doi.org/10.1007/978-3-319-30024-5_9 Go to original source...
    30. Rácz, G., Sali, A., & Schewe, K. D. (2018). Refining semantic matching for job recruitment: An application of formal concept analysis. In International Symposium on Foundations of Information and Knowledge Systems (pp. 322-339). Springer. https://doi.org/10.1007/978-3-319-90050-6_18 Go to original source...
    31. Ricci, F., Rokach, L., & Shapira, B. (2015). Recommender systems: introduction and challenges. In Recommender systems handbook (pp. 1-34). Springer. https://doi.org/10.1007/978-1-4899-7637-6_1 Go to original source...
    32. Robertson, S. E., Walker, S., Jones, S., Hancock-Beaulieu, M. M., & Gatford, M. (1995). Okapi at TREC-3. https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/okapi_trec3.pdf Go to original source...
    33. Al-Otaibi, S., & Ykhlef, M. (2012). A survey of job recommender systems. International Journal of Physical Sciences, 7(29). https://doi.org/10.5897/ijps12.482 Go to original source...
    34. Tran, M. L., Nguyen, A. T., Nguyen, Q. D., & Huynh, T. (2017). A comparison study for job recommendation. In 2017 International Conference on Information and Communications (ICIC) (pp. 199-204). IEEE. https://doi.org/10.1109/INFOC.2017.8001667 Go to original source...
    35. Upadhyay, C., Abu-Rasheed, H., Weber, C., & Fathi, M. (2021). Explainable Job-Posting Recommendations Using Knowledge Graphs and Named Entity Recognition. In 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 3291-3296). IEEE. https://doi.org/10.1109/SMC52423.2021.9658757 Go to original source...
    36. Wang, D., Liang, Y., Xu, D., Feng, X., & Guan, R. (2018). A content-based recommender system for computer science publications. Knowledge Based Systems, 157, 1-9. https://doi.org/10.1016/j.knosys.2018.05.001 Go to original source...
    37. Yahiaoui, L., Boufaïda, Z., & Prié, Y. (2006). Semantic Annotation of Documents Applied to E-Recruitment. In Proceedings of the 3rd Italian Semantic Web Workshop. CEUR Workshop Proceedings. https://ceur-ws.org/Vol-201/10.pdf
    38. Yi, X., Allan, J., & Croft, W. B. (2007). Matching resumes and jobs based on relevance models. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 809-810). ACM. https://doi.org/10.1145/1277741.1277920 Go to original source...
    39. Zhai, C. (2008). Statistical language models for information retrieval. Springer. https://doi.org/10.1007/978-3-031-02130-5 Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content