Representation of Job-Skill in Artificial Intelligence with Knowledge Graph Analysis

Ping Zhao · Chang Liu · Lianying Sun · Tao Peng

30 citations0/0 files storedIEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)

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AI and HR Technologies Higher Education and Employability Information Systems Education and Curriculum Development

Abstract

This study analyses the relationship of different key skills of artificial intelligence (AI) used in the job market. For this, we represent it with a knowledge graph and use a Long Short-term Memory Network to study interactions between these key skills. First, a knowledge graph is build with a rule-based method about these skills in the job markets. Then, the graph is visualized to discover knowledge relationship. Jobs in AI can be classified into two categories: general algorithm jobs and specific focus jobs. Skills of different jobs in AI are very different. Python and Linux are the most necessary key skills for all jobs in AI. Revealing all these key skills in jobs in AI is useful and provide a guideline for job-seekers, companies and universities.

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References

2016Data mining approach to monitoring the requirements of the job market: A case study 2018Measuring the Popularity of Job Skills in Recruitment Market: A Multi-Criteria Approach

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