Concept discovery is a multi-relational data mining task for inducing definitions of a specific relation in terms of other relations in the data set. Such learning tasks usually have to deal with large search spaces and hence have efficiency and scalability issues. In this paper, we present a hybrid approach that combines association rule mining methods and graph-based approaches to cope with these issues. The proposed method inputs the data in relational format, converts it into a graph representation, and traverses the graph to find the concept descriptors. Graph traversal and pruning are guided based on association rule mining techniques. The proposed method distinguishes from the state-of-the art methods as it can work on n-ary relations, it uses path finding queries to extract concepts and can handle numeric values. Experimental results show that the method is superior to the state-of-the art methods in terms of accuracy and the coverage of the induced concept descriptors and the running time.