Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.07233 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912324379475968 |
|---|---|
| author | Fettach, Yousra Bahaj, Adil Ghogho, Mounir |
| author_facet | Fettach, Yousra Bahaj, Adil Ghogho, Mounir |
| contents | Rapid technological advancements pose a significant threat to a large portion of the global workforce, potentially leaving them behind. In today's economy, there is a stark contrast between the high demand for skilled labour and the limited employment opportunities available to those who are not adequately prepared for the digital economy. To address this critical juncture and gain a deeper and more rapid understanding of labour market dynamics, in this paper, we approach the problem of skill need forecasting as a knowledge graph (KG) completion task, specifically, temporal link prediction. We introduce our novel temporal KG constructed from online job advertisements. We then train and evaluate different temporal KG embeddings for temporal link prediction. Finally, we present predictions of demand for a selection of skills practiced by workers in the information technology industry. The code and the data are available on our GitHub repository https://github.com/team611/JobEd. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07233 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Skill Demand Forecasting Using Temporal Knowledge Graph Embeddings Fettach, Yousra Bahaj, Adil Ghogho, Mounir Computers and Society Rapid technological advancements pose a significant threat to a large portion of the global workforce, potentially leaving them behind. In today's economy, there is a stark contrast between the high demand for skilled labour and the limited employment opportunities available to those who are not adequately prepared for the digital economy. To address this critical juncture and gain a deeper and more rapid understanding of labour market dynamics, in this paper, we approach the problem of skill need forecasting as a knowledge graph (KG) completion task, specifically, temporal link prediction. We introduce our novel temporal KG constructed from online job advertisements. We then train and evaluate different temporal KG embeddings for temporal link prediction. Finally, we present predictions of demand for a selection of skills practiced by workers in the information technology industry. The code and the data are available on our GitHub repository https://github.com/team611/JobEd. |
| title | Skill Demand Forecasting Using Temporal Knowledge Graph Embeddings |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2504.07233 |