This paper is concerned with the problems of understanding the relations in automatically extracted semantic datasets such as DBpedia and utilizing them in semantic queries such as SPARQL. Although DBpedia has achieved a great success in supporting convenient navigation and complex queries over the extracted semantic data from Wikipedia, the browsing mechanism and the organization of the relations in the extracted data are far from satisfactory. Some relations have anomalous names and are hard to be understood even by experts if looking at the relation names only; there exist synonymous and polysemous relations which may cause incomplete or noisy query results. In this paper, we propose to solve these problems by 1) exploiting the Wikipedia category system to facilitate relation understanding and query constraint selection, 2) exploring various relation representation models for similar/super-/sub-relation detection to help the users select proper relations in their queries. A prototype system has been implemented and extensive experiments are performed to illustrate the effectiveness of the proposed approach.