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Complex ontology alignment for autonomous systems via the Compact Co-Evolutionary Brain Storm Optimization algorithm.

Xingsi Xue1

  • 1Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, Fujian, 350118, China; Intelligent Information Processing Research Center, Fujian University of Technology, Fuzhou, Fujian, 350118, China.

ISA Transactions
|June 16, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new Compact Co-Evolutionary Brain Storm Optimization (CCBSO) algorithm to address ontology alignment challenges in autonomous systems. CCBSO effectively handles complex correspondences, outperforming existing methods in aligning autonomous system ontologies.

Keywords:
Autonomous systemCompact Co-Evolutionary Brain Storm Optimization AlgorithmComplex ontology alignmentHybrid confidence measure

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Semantic Web Technologies

Background:

  • Autonomous Systems (ASs) require data sharing for cooperation in dynamic environments, typically using ontologies.
  • Ontology heterogeneity, arising from differing concepts and contexts, hinders AS interoperability.
  • Existing one-to-one ontology alignment lacks expressiveness for complex heterogeneity.

Purpose of the Study:

  • To formally define the autonomous system ontology alignment problem.
  • To propose a novel algorithm for handling complex one-to-many and many-to-many correspondences.
  • To improve cooperation and interoperability between autonomous systems.

Main Methods:

  • Formal definition of the autonomous system ontology aligning problem.
  • Development of a hybrid confidence measure for simple and complex correspondences.
  • Implementation of a Compact Co-Evolutionary Brain Storm Optimization (CCBSO) algorithm tailored for ontology alignment.

Main Results:

  • The proposed CCBSO algorithm effectively addresses complex correspondence types (one-to-many, many-to-many).
  • CCBSO demonstrated superior performance compared to state-of-the-art techniques on both simple and complex ontology alignment tasks.
  • Experimental validation on diverse AS ontology aligning tasks confirmed CCBSO's efficacy.

Conclusions:

  • The CCBSO algorithm offers a robust solution for autonomous system ontology alignment.
  • This approach enhances interoperability by effectively managing semantic heterogeneity.
  • CCBSO represents a significant advancement in enabling seamless cooperation among autonomous systems.