Business data are subject to change by time or by the modifications of business rules. New knowledge needs to be extracted to reflect the most up to date situations hence periodic or occasional re-mining is essential. This paper proposes an active multidimensional association mining framework that incorporates with user preference ontology, which contains surrogate queries that represent frequently used queries in the query history log. The representative power and the user preference of the surrogate queries are derived and expressed in fuzzy linguistic terms. The construction of the ontology is demonstrated. How it can assist the active mining mechanism is also described. Specifically, the connection of the user preference ontology to the user profile in the enterprise database allows dispatching of new mining results to specific users automatically. A prototype implementation of the proposed system framework is provided and an effectiveness experiment for the user preference ontology is also conducted.