A context memory model and an approach for context query and association discovery are proposed. The context query is based on a resource description framework （RDF） dataset and SPARQL language. To discover collaborative associations, an approach of transforming RDF named graphs into ＂context graph＂ is proposed. First, the definitions of the importance of the nodes and the weight assignment for the ＂context graph＂ are given. Secondly, the implementation of a spread activation algorithm based on ＂context graph＂ is proposed. An infrastructure is also built up in the collaborative context space （CCS） system to support context memory and knowledge discovery in a collaborative environment.
