Summary
Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Managing Interoperability and Complexity in Health Systems”.
Background: The need for complementary access to multiple RDF databases has fostered new lines of research, but also entailed new challenges due to data representation disparities. While several approaches for RDF-based database integration have been proposed, those focused on schema alignment have become the most widely adopted. All state-of-the-art solutions for aligning RDF-based sources resort to a simple technique inherited from legacy relational database integration methods. This technique – known as element-to-element (e2e) mappings – is based on establishing 1:1 mappings between single primitive elements – e.g. concepts, attributes, relationships, etc. – belonging to the source and target schemas. However, due to the intrinsic nature of RDF – a representation language based on defining tuples < subject, predicate, object > –, one may find RDF elements whose semantics vary dramatically when combined into a view involving other RDF elements – i.e. they depend on their context. The latter cannot be adequately represented in the target schema by resorting to the traditional e2e approach. These approaches fail to properly address this issue without explicitly modifying the target ontology, thus lacking the required expressiveness for properly reflecting the intended semantics in the alignment information.
Objectives: To enhance existing RDF schema alignment techniques by providing a mechanism to properly represent elements with context-dependent semantics, thus enabling users to perform more expressive alignments, including scenarios that cannot be adequately addressed by the existing approaches.
Methods: Instead of establishing 1:1 correspondences between single primitive elements of the schemas, we propose adopting a view-based approach. The latter is targeted at establishing mapping relationships between RDF subgraphs – that can be regarded as the equivalent of views in traditional databases –, rather than between single schema elements. This approach enables users to represent scenarios defined by context-dependent RDF elements that cannot be properly represented when adopting the currently existing approaches.
Results: We developed a software tool implementing our view-based strategy. Our tool is currently being used in the context of the European Commission funded p-medicine project, targeted at creating a technological framework to integrate clinical and genomic data to facilitate the development of personalized drugs and therapies for cancer, based on the genetic profile of the patient. We used our tool to integrate different RDF-based databases – including different repositories of clinical trials and DICOM images – using the Health Data Ontology Trunk (HDOT) ontology as the target schema.
Conclusions: The importance of database integration methods and tools in the context of biomedical research has been widely recognized. Modern research in this area – e.g. identification of disease biomarkers, or design of personalized therapies – heavily relies on the availability of a technical framework to enable researchers to uniformly access disparate repositories. We present a method and a tool that implement a novel alignment method specifically designed to support and enhance the integration of RDF-based data sources at schema (metadata) level. This approach provides an increased level of expressiveness compared to other existing solutions, and allows solving heterogeneity scenarios that cannot be properly represented using other state-ofthe-art techniques.
Keywords
Ontology alignment - database integration - RDF
