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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Graph-Based Semantic Web Service Composition for Healthcare Data Integration.

Ngamnij Arch-Int¹, Somjit Arch-Int¹, Suphachoke Sonsilphong²

¹Semantic Mining and Information Integration Laboratory (SMIIL), Department of Computer Science, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand.

Journal of Healthcare Engineering

|October 26, 2017

Summary

This summary is machine-generated.

This study introduces a graph-based system for automatic semantic web services composition, improving complex business process integration. The novel approach enhances semantic matching for large-scale service discovery and efficient healthcare data integration.

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Area of Science:

Computer Science
Information Systems
Artificial Intelligence

Background:

Automatic web service composition is crucial for complex business processes.
Existing methods lack autonomous semantic matching for large-scale service integration.
Semantic Web Services composition requires robust matchmaking for parameters.

Purpose of the Study:

To propose a novel graph-based Semantic Web Services composition system.
To address the limitations of current functional web services composition approaches.
To enhance the autonomous querying of semantic matches within web service parameters.

Main Methods:

Developed a two-subsystem system: management time and run time.
Management-time subsystem generates dependency graphs using semantic matchmaking rules.
Run-time subsystem employs a graph-based search for discovering nonredundant web service compositions.

Main Results:

The system successfully applied semantic matchmaking for web service composition.
Demonstrated efficient discovery of potential and nonredundant web services.
Evaluated performance through execution time and correctness measurements in healthcare data integration.

Conclusions:

The proposed graph-based system enhances automatic web service composition.
The approach improves semantic matching capabilities for complex service integration.
Validated effectiveness in real-world healthcare data integration scenarios.