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Related Experiment Video

Updated: Apr 13, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Automatically exposing OpenLifeData via SADI semantic Web Services.

Alejandro Rodríguez González¹, Alison Callahan², José Cruz-Toledo³

¹Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid, Madrid, Spain.

Journal of Biomedical Semantics

|May 5, 2015

Summary

This summary is machine-generated.

Semantic Web Services simplify access to Linked Data by automatically configuring services from metadata. This approach enhances data integration and query efficiency in bioinformatics workflows.

Keywords:

Bio2RDF Galaxy OpenLifeData SADI SHARE SPARQL Semantic web services Sentient knowledge explorer

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Bioinformatics
Semantic Web Technologies
Data Integration

Background:

Emerging trends in bioinformatics involve Linked Data (LD) via SPARQL endpoints and Web Services (WS) for reproducible research.
Traditional WS struggle to describe SPARQL endpoint content, hindering Linked Data discovery and integration.
Expertise is needed for complex SPARQL queries, limiting broader data accessibility.

Purpose of the Study:

To propose content-driven Semantic Web Services (SWS) for facile discovery of Linked Data.
To enable seamless integration of Linked Data into bioinformatics workflows.
To simplify data access and querying without prior knowledge of data structure.

Main Methods:

Utilized the OpenLifeData Linked Dataset and its metadata.
Automatically configured over 22,000 SWS using the SADI design principles.
Registered SWS in the SHARE registry for discoverability.
Compared SWS-mediated data access with traditional SPARQL querying.

Main Results:

Demonstrated automatic configuration of numerous SWS from Linked Data metadata.
Showcased SWS discoverability and integration into bioinformatics workflows via the SHARE registry.
Confirmed simplification of data retrieval and protection against resource-intensive queries compared to SPARQL.
Validated data recovery from OpenLifeData without prior knowledge of SPARQL endpoint structure.

Conclusions:

Content-driven SWS enable access to Linked Data without prior knowledge of endpoint specifics.
Integration with clients like SHARE significantly reduces the complexity of federated SPARQL queries.
This approach democratizes access to complex biological datasets.