JobSense: A Data-Driven Career Knowledge Exploration Framework and System

Abstract

Today's job market sees rapid changes due to technology and business model disruptions. To fully tap on one's potential in career development, one has to acquire job and skill knowledge through working on different jobs. Another approach is to seek consultation with career coaches who are trained to offer career advice in various industry sectors. The above two approaches, nevertheless, suffer from several shortcomings. The on-the-job career development approach is highly inefficient for today's fast changing job market. The latter career coach assisted approach could help to speed up knowledge acquisition but it relies on expertise of career coaches but experienced career coaches are scarce, and they too require update of jobs and skills knowledge. Meanwhile, with wide adoption of Online Professional Net-works (OPNs) such as LinkedIn, Xing and others, publicly shared user profiles have become a treasure trove of job and skill related data. Job and skill related information is also hidden in the sea of online job posts and ads. Manually exploring and acquiring knowledge from these varieties of information are daunting and time-consuming. On the other hand, one needs substantial effort to personalize the acquired knowledge to his/her career interests. There is a dire need for a self-help tool to ease this knowledge acquisition and exploration problems. Before that, there is also a need to create and maintain a large knowledge base of these jobs, skills and careers. Our data-driven, automated knowledge acquisition and interactive exploration system, JobSense, would help users meet the above challenges. JobSense enables users at several stages of career, to explore this knowledge at ease via interactive search, easy navigation, bookmarking of information entities and personalized suggestions. Also we have introduced a career path generation module, to return relevant career paths to the users.