A core problem in data mining is to retrieve data in a easy and human friendly way. Automatically translating natural language questions into SQL queries would allow for the design of effective and useful database systems from a user viewpoint. Interesting previous work has been focused on the use of machine learning algorithms for automatically mapping natural language (NL) questions to SQL queries. In this paper, we present many structural kernels and their combinations for inducing the relational semantics between pairs of NL questions and SQL queries. We measure the effectiveness of such kernels by using them in Support Vector Machines to select the queries that correctly answer to NL questions. Experimental results on two different datasets show that our approach is viable and that syntactic information under the form of pairs of syntactic tree fragments (from queries and questions) plays a major role in deriving the relational semantics between the two languages.