Knowledge-driven Unsupervised Skills Extraction for Graph-based Talent Matching
Author:
Affiliation:
1. International Hellenic University, Greece
2. Ionian University, Greece
Funder
European Commission
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3549737.3549769
Reference31 articles.
1. AI in talent acquisition: a review of AI-applications used in recruitment and selection
2. Akhil Arora Alberto Garcia-Duran and Robert West. 2021. Low-Rank Subspaces for Unsupervised Entity Linking. (2021) 8037-8054. https://doi.org/10.18653/v1/2021.emnlp-main.634 arxiv:2104.08737 10.18653/v1
3. Akhil Arora Alberto Garcia-Duran and Robert West. 2021. Low-Rank Subspaces for Unsupervised Entity Linking. (2021) 8037-8054. https://doi.org/10.18653/v1/2021.emnlp-main.634 arxiv:2104.08737
4. Towards ESCO 4.0 – Is the European classification of skills in line with Industry 4.0? A text mining approach
5. Skills2Job: A recommender system that encodes job offer embeddings on graph databases
Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Job Role Description and Skill Matching in a Rapidly Changing Labor Market Using Knowledge Engineering;Springer Proceedings in Business and Economics;2024
2. Detection of Candidate Skills from Job Offers and Comparison with ESCO Database;Lecture Notes in Computer Science;2024
3. Connecting Learning Material and the Demand of the Job Market Using Artificial Intelligence;Artificial Intelligence in Education Technologies: New Development and Innovative Practices;2023
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