文献类型：会议论文

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, T[1]

机构：[1]Beijing Union Univ, Smart City Coll, Beijing, Peoples R China;[2]Beijing Union Univ, Coll Robot, Beijing, Peoples R China;[3]Beijing Union Univ, Coll Urban Rail Transit & Logist, Beijing, Peoples R China

第一机构：北京联合大学继续教育学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Robot, Beijing, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;

会议论文集：IEEE International Symposium on Product Compliance Engineering-Asia (IEEE ISPCE-EN)

会议日期：DEC 05-07, 2018

会议地点：PEOPLES R CHINA

语种：英文

外文关键词：Artificial Intelligence Knowledge Graph

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.