Improving Personalization and Contextualization of Queries to Knowledge Bases Using Spreading Activation and Users' Feedback.

Facilitating knowledge acquisition when users are consulting knowledge bases (KB) is often a challenge, given the large amount of data contained. Providing users with appropriate contextualization and personalization of the content of KBs is a way to try to achieve this goal. This paper presents a mechanism intended to provide contextualization and personalization of queries to KBs based on collected data regarding users’ preferences, both implicitly (users’ profiles) and explicitly (users’ feedback). This mechanism combines user data with a spreading activation (SA) algorithm to generate the contextualization. The initial positive results of the evaluation of the contextualization are presented in this paper.