This paper describes the application of relational learning to interactive document retrieval. In this model, retrieval systems support users to find documents effectively through relevance feedback. At present vector space model is a typical representation method to realize relevance feedback. However it can neither express relationship such as proximity nor keep several features separately. We supplement these defects with a set of rules, which are constructed by relational learning and used to identify relevant documents. The learning algorithm consists of separate-and-conquer strategy and top-down heuristic search with limited backtracking. Background relations are made only from keywords, thus constructed rules represent useful keyword combinations to search relevant documents. We evaluate the effectiveness of our approach on a document retrieval experiment using a test bed database. The results show our method enhances both effectiveness and efficiency compared to a normal method with only query vector. We finally consider the effect and the cost of rule making.