The design and implementation of a meaning driven data query language

We present the design and implementation of a meaning driven data query language-MDDQL-which aims at the construction of queries through system made suggestions of natural language based query terms for both scientific application domain terms and operator/operation terms. A query construction blackboard is used where query language terms are suggested to the user in its preferred natural language and in a name centered way, together with their connotation. This helps in understanding the meaning of the terms and/or operators or operations to be included in the query. Furthermore, the construction of the query turns out to be an incremental refinement of the query under construction through semantic constraints, where only those domain language terms and/or operators/operations are suggested which result into meaningful combinations of query terms as related to the scientific application domain semantics. Therefore, semantically meaningless queries can be prevented during the query construction. Such a semantics aware mechanism is not available in conventional database query languages such as SQL, where one is allowed to execute a query calculating, for example, the average of numerical data values whereas they represent the codes of categorical values. Moreover no familiarity with the semantics of complex database schemes or interpretation of the symbols (names of classes/tables/attributes, value codes) underlying the storage model, as well as familiarity with the syntax of a database specific query language are needed by the end-user.