Contextualization and Personalization of Queries to Knowledge Bases Using Spreading Activation

Most taxonomies and thesauri offer their users a huge amount of structured data. However, this volume of data is often excessive, and, thus does not fulfill the needs of the users, who are trying to find specific information related to a certain concept. While there are techniques that may partially alleviate this problem (e.g. visual representation of the data), some of the effects of the information overload persist. This paper proposes a mechanism based on spreading activation for the personalization and knowledge extraction, derived from user activities. More precisely, the system extracts contextualization by using a spreading activation algorithm. The preliminary results of this approach are presented in this paper.