Skill Demand Forecasting Using Temporal Knowledge Graph Embeddings

• URL: http://arxiv.org/abs/2504.07233v2
• Date: Mon, 14 Apr 2025 10:43:04 GMT
• Title: Skill Demand Forecasting Using Temporal Knowledge Graph Embeddings
• Authors: Yousra Fettach, Adil Bahaj, Mounir Ghogho,
• Abstract summary: We introduce our novel temporal KG constructed from online job advertisements.<n>We then train and evaluate different temporal KG embeddings for temporal link prediction.<n>We present predictions of demand for a selection of skills practiced by workers in the information technology industry.
• Score: 3.1515385358176817
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• Information Retrieval in the Age of Generative AI: The RGB Model [77.96475639967431]
  This paper presents a novel quantitative approach to shed light on the complex information dynamics arising from the growing use of generative AI tools. We propose a model to characterize the generation, indexing, and dissemination of information in response to new topics. Our findings suggest that the rapid pace of generative AI adoption, combined with increasing user reliance, can outpace human verification, escalating the risk of inaccurate information proliferation.
  arXiv  Detail & Related papers  (2025-04-29T10:21:40Z)
• Data to Decisions: A Computational Framework to Identify skill requirements from Advertorial Data [1.5621498886998335]
  The proposed framework uses techniques of statistical analysis, data mining and natural language processing for the purpose.<n>The analytical results not only provide useful insights about current state of skill needs in CS&IT industry but also provide practical implications to prospective job applicants, training agencies, and institutions of higher education & professional training.
  arXiv  Detail & Related papers  (2025-03-21T09:49:31Z)
• Neural-Symbolic Reasoning over Knowledge Graphs: A Survey from a Query Perspective [55.79507207292647]
  Knowledge graph reasoning is pivotal in various domains such as data mining, artificial intelligence, the Web, and social sciences.<n>The rise of Neural AI marks a significant advancement, merging the robustness of deep learning with the precision of symbolic reasoning.<n>The advent of large language models (LLMs) has opened new frontiers in knowledge graph reasoning.
  arXiv  Detail & Related papers  (2024-11-30T18:54:08Z)
• CAPER: Enhancing Career Trajectory Prediction using Temporal Knowledge Graph and Ternary Relationship [23.845300607433792]
  We propose a novel solution, named as CAPER, that solves the challenges via sophisticated temporal knowledge graph (TKG) modeling. It enables the utilization of a graph-structured knowledge base with rich expressiveness, effectively preserving the changes in job movement patterns. Experiments on a real-world career trajectory dataset demonstrate that CAPER consistently and significantly outperforms four baselines.
  arXiv  Detail & Related papers  (2024-08-28T08:21:56Z)
• A Cross-View Hierarchical Graph Learning Hypernetwork for Skill Demand-Supply Joint Prediction [24.737433570616297]
  We propose a Cross-view Hierarchical Graph learning Hypernetwork (CHGH) framework for joint skill demand-supply prediction. Specifically, CHGH is an encoder-decoder network consisting of i) a cross-view graph encoder to capture the interconnection between skill demand and supply, ii) a hierarchical graph encoder to model the co-evolution of skills from a cluster-wise perspective, andiii) a conditional hyper-decoder to jointly predict demand and supply variations.
  arXiv  Detail & Related papers  (2024-01-31T13:56:08Z)
• Iterative Zero-Shot LLM Prompting for Knowledge Graph Construction [104.29108668347727]
  This paper proposes an innovative knowledge graph generation approach that leverages the potential of the latest generative large language models. The approach is conveyed in a pipeline that comprises novel iterative zero-shot and external knowledge-agnostic strategies. We claim that our proposal is a suitable solution for scalable and versatile knowledge graph construction and may be applied to different and novel contexts.
  arXiv  Detail & Related papers  (2023-07-03T16:01:45Z)
• The Future of ChatGPT-enabled Labor Market: A Preliminary Study in China [22.120632047061235]
  We study the future of ChatGPT-enabled labor market from the view of Human-AI Symbiosis instead of Human-AI Confrontation. Results indicate that about 28% of occupations in the current labor market require ChatGPT-related skills. Additional 45% occupations in the future will require ChatGPT-related skills.
  arXiv  Detail & Related papers  (2023-04-14T06:56:35Z)
• Forecasting Future World Events with Neural Networks [68.43460909545063]
  Autocast is a dataset containing thousands of forecasting questions and an accompanying news corpus. The news corpus is organized by date, allowing us to precisely simulate the conditions under which humans made past forecasts. We test language models on our forecasting task and find that performance is far below a human expert baseline.
  arXiv  Detail & Related papers  (2022-06-30T17:59:14Z)
• What Should I Know? Using Meta-gradient Descent for Predictive Feature Discovery in a Single Stream of Experience [63.75363908696257]
  computational reinforcement learning seeks to construct an agent's perception of the world through predictions of future sensations. An open challenge in this line of work is determining from the infinitely many predictions that the agent could possibly make which predictions might best support decision-making. We introduce a meta-gradient descent process by which an agent learns what predictions to make, 2) the estimates for its chosen predictions, and 3) how to use those estimates to generate policies that maximize future reward.
  arXiv  Detail & Related papers  (2022-06-13T21:31:06Z)
• Practical Skills Demand Forecasting via Representation Learning of Temporal Dynamics [4.536775100566484]
  Rapid technological innovation threatens to leave much of the global workforce behind. Governments and markets must find ways to quicken the rate at which the supply of skills reacts to changes in demand. This paper presents a pipeline which makes one-shot multi-step forecasts into the future using a decade of monthly skill demand observations.
  arXiv  Detail & Related papers  (2022-05-18T04:02:55Z)
• Policy Gradients Incorporating the Future [66.20567145291342]
  We introduce a method that allows an agent to "look into the future" without explicitly predicting it. We propose to allow an agent, during its training on past experience, to observe what emphactually happened in the future at that time. This gives our agent the opportunity to utilize rich and useful information about the future trajectory dynamics in addition to the present.
  arXiv  Detail & Related papers  (2021-08-04T14:57:11Z)
• Data Considerations in Graph Representation Learning for Supply Chain Networks [64.72135325074963]
  We present a graph representation learning approach to uncover hidden dependency links. We demonstrate that our representation facilitates state-of-the-art performance on link prediction of a global automotive supply chain network.
  arXiv  Detail & Related papers  (2021-07-22T12:28:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.