Abstract
In recent years, the tremendous increase in the use of medical knowledge-discovery and decision-support applications has often required clinical researchers to write complex database queries. The users of these data analysis systems are normally unaware of the semantic relationships between the concepts stored in a database. In order to provide automated query formulation services, some mechanism for generating queries is required. In this regard, as reported in [1], domain ontologies can be used to formulate relational database queries in order to simplify the data access of the underlying data sources. However, the provision of such a query generation facility requires managing complex mappings between domain ontologies and relational data sources. In this regard, this paper discusses our approach to define mappings between domain ontologies and database schemas to support the ontology assisted relational query formulation process. This approach has been applied to the integrated medical database schema of the EU funded Health-e-Child (HeC) project.
Highlights
This paper reports on further developments in this area and focuses on (1) domain metadata representation from a relational model to an ontology model to enable ontology based query formulation; (2) storage and retrieval of ontology-database mappings to translate ontology statements into relational query statements as per the underlying schema structure; and (3) an ontology assisted query formulation case study in relation to the Health-e-Child [4] project. 1.3
One of the major requirements of an ontology assisted query formulation system is the formulation of a domain ontology which includes definition of domain metadata, relationships and knowledge of the ontology
An ontology modelling approach has been identified to transform domain metadata and relationships into the ontology schema to assist in the query formulation process
Summary
The Problem in General In information management systems, structured query formulation languages are one of the means to retrieve information. In contrast to the menu driven (MD) or query by example (QBE) information access methods [2], writing structured queries is a powerful method to access data because it allows end-users to formulate complex database queries and this forces end-users to learn specialised query languages. Structured query formulation, with the exception of a few visual query generation approaches, remains noticeably difficult for large classes of end users. Information technology today 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 in providing visual tools to generate specific queries. Little has been achieved in the use of computational techniques to provide users with ‘query formulation’ services using ‘domain ontologies’
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