文献类型：期刊文献

英文题名：Representation of job-skill in artificial intelligence with knowledge graph analysis

作者：Jia, Shanshan[1]; Liu, Xiaoan[1]; Zhao, Ping[1]; Liu, Chang[2]; Sun, Lianying[3]; Peng, Tao[2]

第一作者：Jia, Shanshan

通讯作者：Peng, Tao

机构：[1] Smart City College, Beijing Union University, Beijing, China; [2] College of Robotics, Beijing Union University, Beijing, China; [3] College of Urban Rail Transit and Logistics, Beijing Union University, Beijing, China

第一机构：北京联合大学智慧城市学院

年份：2018

外文期刊名：ISPCE-CN 2018 - IEEE International Symposium on Product Compliance Engineering - Asia

收录：EI(收录号：20193707429650)

语种：英文

外文关键词：Computer operating systems - Commerce - Employment

摘要：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. ? 2018 IEEE.