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Linked Data Applications Through Ontology Based Data Access in Clinical Research.

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Studies in Health Technology and Informatics
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Summary
This summary is machine-generated.

Bridging semantic gaps in clinical data is crucial. Ontology Based Data Access (OBDA) integrates diverse electronic healthcare records (EHR) and research data, enabling unified querying and analysis for better biomedical insights.

Keywords:
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Area of Science:

  • Biomedical Informatics
  • Data Science
  • Semantic Web Technologies

Background:

  • Clinical and research data are fragmented across isolated systems with heterogeneous data models.
  • Biomedicine increasingly uses Semantic Web technologies and Linked Data for sophisticated applications.
  • Electronic Healthcare Records (EHR) data reside in diverse clinical subsystems with proprietary relational database schemas, hindering semantic integration.

Purpose of the Study:

  • To describe methods for using Ontology Based Data Access (OBDA) to bridge the semantic gap between raw data and user-oriented views.
  • To demonstrate the advantages of semantic mediation for accessing and querying dispersed clinical and research data.
  • To highlight the benefits of abstracting low-level data details for improved data accessibility.

Main Methods:

  • Utilizing OBDA to create mappings between data schemas and ontologies.
  • Generating materialized or virtualized RDF triples from existing data sources.
  • Applying OBDA to CentraXX for biobank and study management.

Main Results:

  • OBDA effectively bridges the semantic gap, enabling ontology-based queries across heterogeneous data sources.
  • Experiments with CentraXX demonstrated the advantages of semantic abstraction and mediation.
  • Virtualized RDF triples facilitate querying and processing of integrated clinical and research data.

Conclusions:

  • OBDA provides a viable solution for integrating dispersed clinical and research data.
  • Abstraction from low-level details and semantic mediation are crucial for effective data access.
  • Professional platforms are recommended for Linked Data applications due to complexity, user experience, and performance considerations.