To effectively match the biomedical ontologies, a CESA-based biomedical ontology matching technique is proposed to determine the identical biomedical concepts, which combines compact Evolutionary Algorithm and the Simulated Annealing algorithm to tackle the biomedical ontology matching problem.
May 12, 2019
Since it is a complex problem, Evolutionary Algorithm (EA) can present a good methodology for matching biomedical ontologies. To improve the efficiency, in this ...
This article models the ontology alignment problem as optimizing the fitness of a state whose optimum is obtained by using the simulated annealing. A complex ...
In this paper, we propose a compact hybrid Evolutionary Algorithm (chEA), which utilizes a probabilistic representation of the population to perform the ...
Get details about the chapter of Matching Biomedical Ontologies Through Compact Evolutionary Simulated Annealing Algorithm from book Genetic and ...
The basics of EA based ontology matching technology are introduced, the state-of-the-art EA based ontology matching approach is discussed, ...
A multi-objective evolutionary algorithm with a relevance matrix (MOEA-RM) is proposed to address it. In particular, a relevance matrix (RM) is presented.
Missing: Simulated | Show results with:Simulated
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Mar 24, 2021 · To address this problem, sensor ontology matching is introduced to establish the corresponding relationship between different sensor terms.
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Matching Biomedical Ontologies Through Compact Hybrid Evolutionary Algorithm. 111 of Anatomy (FMA) [6] uses the entity “Cardiac Muscle Tissue” to describe ...
Missing: Simulated Annealing