{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T11:28:34Z","timestamp":1743074914965,"version":"3.40.3"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031806063"},{"type":"electronic","value":"9783031806070"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-80607-0_17","type":"book-chapter","created":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T16:35:29Z","timestamp":1735662929000},"page":"214-227","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automating Resume Analysis: Knowledge Graphs via\u00a0Prompt Engineering"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0326-8742","authenticated-orcid":false,"given":"Giorgio","family":"Lazzarinetti","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6406-536X","authenticated-orcid":false,"given":"Sara","family":"Manzoni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7312-7123","authenticated-orcid":false,"given":"Italo","family":"Zoppis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,1]]},"reference":[{"issue":"1","key":"17_CR1","first-page":"1","volume":"2018","author":"J Chen","year":"2018","unstructured":"Chen, J., Zhang, C., Niu, Z.: A two-step resume information extraction algorithm. Math. Probl. Eng. 2018(1), 1\u20138 (2018)","journal-title":"Math. Probl. Eng."},{"key":"17_CR2","unstructured":"Jagwani, V., Meghani, S., Pai, K., Dhage, S.: Resume Evaluation through Latent Dirichlet Allocation and Natural Language Processing for Effective Candidate Selection. arXiv preprint arXiv:2307.15752 (2023)"},{"key":"17_CR3","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1007\/978-3-642-12038-1_17","volume-title":"Databases in Networked Information Systems","author":"S Maheshwari","year":"2010","unstructured":"Maheshwari, S., Sainani, A., Reddy, P.K.: An approach to extract special skills to improve the performance of resume selection. In: Kikuchi, S., Sachdeva, S., Bhalla, S. (eds.) Databases in Networked Information Systems, pp. 256\u2013273. Springer Berlin Heidelberg, Berlin, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-12038-1_17"},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Kopparapu, S.K.: Automatic extraction of usable information from unstructured resumes to aid search. In: 1st IEEE International Conference on Progress in Informatics and Computing (PIC \u201910), vol. 1, pp. 99\u2013103. IEEE, China (2010)","DOI":"10.1109\/PIC.2010.5687428"},{"issue":"12","key":"17_CR5","doi-asserted-by":"publisher","first-page":"8492","DOI":"10.1016\/j.eswa.2010.05.027","volume":"37","author":"X Ji","year":"2010","unstructured":"Ji, X., Zeng, J., Zhang, S., Wu, C.: Tag tree template for Web information and schema extraction. Expert Syst. Appl. 37(12), 8492\u20138498 (2010)","journal-title":"Expert Syst. Appl."},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Rasal, P., Balwaik, Y.: Resume parser analysis using machine learning and natural language processing. Int. Res. J. Modern. Eng. Technol. Sci. 5(3), (2023)","DOI":"10.22214\/ijraset.2023.52202"},{"key":"17_CR7","unstructured":"Bhaliya, N., Gandhi, J., Singh, D.K.: NLP based Extraction of Relevant Resume using Machine Learning. IJITEE (2020)"},{"key":"17_CR8","unstructured":"Bhor, S., Gupta, V., Nair, V., Shinde, H., Kulkarni, M.: Resume parser using NLP Techniques. www.ijres.org9(6), 01\u201306 (2021)"},{"key":"17_CR9","unstructured":"de Groot, M., Schutte, J., Graus, D.: Job posting-enriched knowledge graph for skills-based matching. In: RECSYS in HR 2022, Co-located with the 16th ACM Conference on Recommender Systems, US (2022)"},{"key":"17_CR10","doi-asserted-by":"publisher","first-page":"2181","DOI":"10.18653\/v1\/2023.findings-eacl.163","volume-title":"Findings of the Association for Computational Linguistics: EACL 2023","author":"N Goyal","year":"2023","unstructured":"Goyal, N., Kalra, J.S., Sharma, C., Mutharaju, R., Sachdeva, N., Kumaraguru, P.: JobXMLC: extreme multi-label classification of job skills with graph neural networks. In: Vlachos, A., Augenstein, I. (eds.) Findings of the Association for Computational Linguistics: EACL 2023, pp. 2181\u20132191. Association for Computational Linguistics, Dubrovnik, Croatia (2023)"},{"key":"17_CR11","unstructured":"Wang, Y., Allouache, Y., Joubert, C.: Analysing CV corpus for finding suitable candidates using knowledge graph and BERT. In: 13th International Conference on Advances in Databases. Knowledge, and Data Applications (DBKDA 2021), pp. 256\u2013273. Valencia, Spain (2021)"},{"key":"17_CR12","doi-asserted-by":"publisher","unstructured":"Zhong, L., Wu, J., Li, Q., Peng, H., Wu, X.: A comprehensive survey on automatic knowledge graph construction. ACM Comput. Surv. 56, (2023). https:\/\/doi.org\/10.1145\/3618295","DOI":"10.1145\/3618295"},{"key":"17_CR13","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics, Minneapolis, Minnesota (2019)"},{"key":"17_CR14","unstructured":"Liu, Y., et al.: RoBERTa: a robustly optimized bert pretraining approach. In: Proceedings of the 23rd Conference on Empirical Methods in Natural Language Processing (EMNLP 2019), pp. 4248\u20134258. Association for Computational Linguistics, Hong Kong, China (2019)"},{"issue":"1","key":"17_CR15","first-page":"5485","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(1), 5485\u20135551 (2020)","journal-title":"J. Mach. Learn. Res."},{"key":"17_CR16","unstructured":"Amatriain, X.: Prompt Design and Engineering: Introduction and Advanced Methods.arXiv preprint, arXiv:2401.14423 (2024)"},{"key":"17_CR17","doi-asserted-by":"crossref","unstructured":"Yang, J., et al.: Harnessing the power of llms in practice: a survey on ChatGPT and beyond. ACM Trans. Knowl. Discov. Data 18(6), Article 160, 32 pages (2024)","DOI":"10.1145\/3649506"},{"key":"17_CR18","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA (2017)"},{"key":"17_CR19","unstructured":"OpenAI: GPT-4 Technical Report. arXiv preprint. arXiv:2303.08774 (2023)"},{"key":"17_CR20","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown, T., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877\u20131901 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"17_CR21","unstructured":"Wei, J., et al.: Finetuned language models are zero-shot learners. In: International Conference on Learning Representations (ICLR 2022), pp. 1\u20132. (2022)"},{"key":"17_CR22","unstructured":"Wei, J., et al.: Chain-of-thought prompting elicits reasoning in large language models. In: 36th Conference on Neural Information Processing Systems (NeurIPS 2022), pp. 1882\u20131893. (2022)"},{"key":"17_CR23","unstructured":"Lewis, P., et al.: Retrieval-augmented generation for knowledge-intensive NLP tasks. In: Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS 2020), pp. 9459\u20139474. Vancouver, Canada (2020)"},{"key":"17_CR24","unstructured":"Yao, S., et al.: Tree of thoughts: deliberate problem solving with large language models. In: Advances in Neural Information Processing Systems 36 (NeurIPS 2023), NeurIPS (2023)"},{"key":"17_CR25","unstructured":"Besta, M., et al.: Graph of thoughts: solving elaborate problems with large language models. In: 30th International Conference on Neural Information Processing Systems (NeurIPS 2023), pp. 15682\u201315690. ETH Zurich, Zurich, Switzerland (2023)"},{"key":"17_CR26","doi-asserted-by":"crossref","unstructured":"Zaouga, W., Ben Arfa Rabai, L.: Modeling and evaluating a human resource management ontology. In: Computer Science On-line Conference, pp. 1\u20132. (2019)","DOI":"10.1007\/978-3-030-19807-7_37"},{"key":"17_CR27","doi-asserted-by":"crossref","unstructured":"Ahmed, N., Khan, S., Latif, K.: Job description ontology. In: 2016 International Conference on Frontiers of Information Technology (FIT), pp. 217\u2013222. Islamabad, Pakistan (2016)","DOI":"10.1109\/FIT.2016.047"},{"key":"17_CR28","doi-asserted-by":"crossref","unstructured":"Javed, Z., Qazi, H., Khoja, S.A.: An ontology-based knowledge management model for e-recruitment utilizing MOOCs data. In: 2019 8th International Conference on Information and Communication Technologies (ICICT), pp. 124\u2013128. Karachi, Pakistan (2019)","DOI":"10.1109\/ICICT47744.2019.9001911"},{"key":"17_CR29","unstructured":"Kaggle - Resume Entities for NER. https:\/\/www.kaggle.com\/datasets\/dataturks\/resume-entities-for-ner\/data. Accessed 09 June 2024"},{"key":"17_CR30","unstructured":"Hugging Face - FacebookAI - RoBERTa Base Model. https:\/\/huggingface.co\/FacebookAI\/roberta-base. Accessed 26 Aug 2024"},{"key":"17_CR31","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: proceeding of the 3rd International Conference on Learning Representations (ICLR) , San Diego, USA (2015)"},{"key":"17_CR32","unstructured":"Hugging Face - spaCy - en-core-web-sm Model. https:\/\/huggingface.co\/spacy\/en_core_web_sm. Accessed 26 Aug 2024"},{"key":"17_CR33","unstructured":"SpaCy - Initialize Method. https:\/\/spacy.io\/api\/language#initialize. Accessed 26 Aug 2024"},{"key":"17_CR34","unstructured":"Hugging Face - Sentence Transformers. https:\/\/huggingface.co\/sentence-transformers. Accessed 09 June 2024"}],"container-title":["Lecture Notes in Computer Science","AIxIA 2024 \u2013 Advances in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-80607-0_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T17:05:01Z","timestamp":1735664701000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-80607-0_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031806063","9783031806070"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-80607-0_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"1 January 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIxIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference of the Italian Association for Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bolzano","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aiia2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}