Knowledge management applications based on user activities feedback

Abstract

This research is devoted to the problems of searching information in knowledge management system of some company using keywords and categories list. Authors analyzed different approaches to data storage in management systems and several existing KM systems were considered. The necessity of knowledge presenting in simple and understandable form was mentioned, but also the structuring system requirements and self-learning ability was underscored. Authors suggested using request history with categories and keywords analytics to realize the system ability to change keywords (and categories) weight coefficients. It allows the system to be self-learning using user activity results. Associative rules search task was implemented as well. This analysis makes it possible to find keywords bonds inside user requests content, and helps to increase the time of requested knowledge extracting. Besides, the analysis of the user requests statistics helps to determine individuals, who are in charge for filling the system with new materials, which can be helpful for motivational goals.