Social bookmarking Web sites allow users submitting their resources and labeling them with arbitrary keywords, called tags, to create folksonomies. Tag recommendation is an important element of collaborative tagging systems which aims at providing relevant information to users by proposing a set of tags to each newly posted resource. In this paper, we focus on the task of tag recommendation when a user examines a document based on the user's tagging behavior. We explore the use of this semantic relationship in modeling the user tagging behavior. The experiments are performed on the data set obtained from a social bookmarking site. Our experimental result show that our method is efficient in modeling users' tagging behavior and it can be used to recommend tags for resources.