Semantic query in a relational database using a local ontology construction

The use of a structured query formulation language is one way to retrieve information in information management systems. Formulated queries allow the selection of data under particular constraints. In contrast to menu driven or query by example (QBE) information access methods,1 writing structured queries is a powerful method to access data because it allows endusers to formulate complex database queries. However, this method consequently forces endusers to learn specialised query languages. Therefore, structured query formulation, with the exception of a few visual query generation approaches, is difficult for most end-users. Despite the variety of approaches that exists, three major questions are common when information extraction is requested from available data: (1) what type of request can a specific system handle, (2) how can visual interfaces be provided to generate data requests and (3) how can the user be assisted to formulate queries in order to retrieve more accurate information? Nowadays, information technology has been widely adopted in resolving the first two problems by providing some theoretical and practical solutions using artificial intelligence techniques and graph theories, especially by providing visual tools to generate specific queries. However, in the use of computational techniques, there is inadequate information to provide users with query formulation services using domain ontologies. Laborda and Conrad2 introduced a representation format for both schema and data information based on Web ontology language (OWL). The advantage of their approach is obvious – relational data is able to be processed for Semantic Web applications using built-in functionality like query languages or reasoning mechanisms. In fact, with their approach, Semantic Web applications no longer need to implement their own in relation to semantic mapping.