Research On Personalized Knowledge Retrieval In Knowledge Management System

In knowledge economy arrival today, the knowledge management system became an effective tool for the enterprise to implement the knowledge management, it could effectively carry on the organization, management and application of the enterprise knowledge. The knowledge retrieval system in the knowledge management system is an important tool for the staff to acquire and study knowledge. Therefore, enhancing the knowledge retrieval efficiency avails effective improvement of the knowledge study as well as the utilization ability.In the traditional knowledge retrieval system, it could not provide accurate knowledge according to the different user demand, so the accuracy ratio is low. In this thesis, we design a personalized knowledge retrieval system which integrates present knowledge management system and search engine technology according to the knowledge storage situation in knowledge base as well as the knowledge retrieval characteristic, propose an personalized knowledge retrieval system model structure and do research on user profile construction and knowledge filtering technology which are the core technologies in personalized knowledge retrieval. This thesis provides a model of user profile and knowledge document combining term vector space mode and knowledge classification probability model which makes the user profile and knowledge document expression more comprehensive, uses a compositive filtering way which integrates content-base filtering and collaborative filtering on knowledge filtering and propose a personalized ranking algorithm of the retrieval results. Modules in the personalized knowledge retrieval system cooperate to achieve personalization function.