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Integrating Sensor Ontologies with Niching Multi-Objective Particle Swarm Optimization Algorithm.

Yucheng Zhuang1, Yikun Huang2, Wenyu Liu3

  • 1Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, No. 69 Xuefu South Road, Minhou, Fuzhou 350118, China.

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|June 10, 2023
PubMed
Summary
This summary is machine-generated.

A new algorithm, niching multi-objective particle swarm optimization (NMOPSO), improves sensor ontology matching for better data integration. This method enhances semantic relationships between diverse sensor devices, enabling seamless information exchange.

Keywords:
Ontology Alignment Evaluation Initiativemulti-modal optimizationniching multi-objective particle swarm optimization algorithmsensor ontology matching

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Semantic Web Technologies

Background:

  • Sensor ontology facilitates standardized semantic representation for sensor device information sharing.
  • Varied semantic descriptions hinder data exchange between heterogeneous sensor devices.
  • Sensor ontology matching is crucial for data integration and interoperability.

Purpose of the Study:

  • To propose a novel algorithm, NMOPSO, for effective sensor ontology matching.
  • To address the multi-modal nature of sensor ontology meta-matching problems.
  • To enhance data integration and sharing capabilities across diverse sensor networks.

Main Methods:

  • Developed a niching multi-objective particle swarm optimization (NMOPSO) algorithm.
  • Incorporated a niching strategy into MOPSO to find multiple global optimal solutions.
  • Integrated diversity-enhancing and opposition-based learning (OBL) strategies into NMOPSO.

Main Results:

  • NMOPSO effectively solves the sensor ontology matching problem.
  • The algorithm demonstrates improved convergence to real Pareto fronts (PFs).
  • Experimental results show NMOPSO outperforms existing MOPSO-based techniques and OAEI participants.

Conclusions:

  • NMOPSO significantly enhances the quality of sensor ontology matching.
  • The proposed algorithm facilitates better data integration and information sharing between sensor devices.
  • NMOPSO offers a robust solution for semantic interoperability in sensor networks.