Keyword search is a popular technique for querying the ever increasing repositories of RDF graph data because it frees the user from knowing a formal query language and the structure of the data. However, the imprecision of keyword queries results in overwhelming numbers of candidate results making the identification of relevant results challenging and hindering the scalability of the query evaluation algorithms. To address these issues, we introduce cohesive keyword queries on RDF data. Cohesive queries allow the user to flexibly and effortlessly convey her intention using cohesive keyword groups. A cohesive group of keywords in a query indicates that the keywords of the group should form a cohesive unit in the query results. We provide formal semantics of cohesive queries. We design a query evaluation algorithm which relies on the structural summary of the RDF graph to generate pattern graphs that satisfy the cohesiveness constraints. Pattern graphs are structured queries that can be evaluated over the RDF data to compute the query results. Our experiments demonstrate the efficiency of our algorithm and the effectiveness of cohesive keyword queries in improving the result quality and in pruning the space of pattern graphs compared to flat keyword queries. Most importantly, these benefits are achieved while retaining the simplicity and convenience of traditional keyword search.