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Related Experiment Video

Updated: Mar 6, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
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BioFed: federated query processing over life sciences linked open data.

Ali Hasnain1, Qaiser Mehmood2, Syeda Sana E Zainab2

  • 1Insight Centre for Data Analytics, National University of Ireland (NUIG), Galway, Ireland. ali.hasnain@insight-centre.org.

Journal of Biomedical Semantics
|March 17, 2017
PubMed
Summary
This summary is machine-generated.

BioFed federates life science data from over 130 SPARQL endpoints, offering a unified access point. This system optimizes query submission using provenance information, outperforming existing solutions for efficient data retrieval.

Keywords:
Life sciences datasetLinked open dataSPARQL query federation

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Area of Science:

  • Biomedical Informatics
  • Data Science
  • Bioinformatics

Background:

  • Biomedical data is increasingly available as linked data, often in RDF triple format.
  • Unified access to heterogeneous biological data requires querying multiple SPARQL endpoints.
  • Efficient and reliable solutions are needed for life sciences research data retrieval.

Purpose of the Study:

  • To develop a federated query system (BioFed) for accessing diverse life sciences data.
  • To optimize query submission and data retrieval from numerous SPARQL endpoints.
  • To establish a benchmark for federated query systems in the life sciences domain.

Main Methods:

  • BioFed employs efficient cataloguing, triple pattern-wise source selection, and semantic source normalization.
  • It integrates data from newly identified public SPARQL endpoints.
  • The system utilizes SPARQL 1.1 standards and links basic provenance information to retrieved data.

Main Results:

  • BioFed provides a single-point-of-access to over 130 life sciences SPARQL endpoints.
  • It facilitates efficient query generation and returns data with provenance information.
  • Evaluation against FedX shows competitive performance, attributed to provenance-based source selection.

Conclusions:

  • Optimizing source selection is crucial for federated query engines in life sciences.
  • Cataloguing SPARQL endpoints with type and property indexing enhances data querying efficiency.
  • Further improvements can be achieved using ontologies for query term normalization.