A multi-objective particle swarm optimization with density and distribution-based competitive mechanism for sensor ontology meta-matching

Abstract

Sensor ontology is a standard conceptual model that describes information of sensor device, which includes the concepts of various sensor modules and the relationships between them. The problem of heterogeneity between sensor ontologies is introduced because different sensor ontology engineers have different ways of describing sensor devices and different structures for the construction of sensor ontologies. Addressing the heterogeneity of sensor ontologies contributes to facilitate the semantic fusion of two sensor ontologies, enabling the sharing and reuse of sensor information. To solve the above problem, an ontology meta-matching method is proposed by this paper to find out the correspondence between entities in distinct sensor ontologies. How to measure the degree of similarity between entities with a set of suitable similarity measures and how to better integrate multiple measures to determine the equivalent entities are the challenges of the ontology meta-matching problem. In this paper, two approximate measurement methods of the quality for ontology matching results are designed, and a multi-objective optimization model for the ontology meta-matching problem is constructed based on these methods. Eventually, a multi-objective particle swarm optimization (MOPSO) algorithm is propounded to dispose of the problem and optimize the quality of ontology meta-matching results, which is named density and distribution-based competitive mechanism multi-objective particle swarm algorithm (D$$^{2}$$ 2 CMOPSO). The sophistication of the D$$^{2}$$ 2 CMOPSO based sensor ontology meta-matching method is verified through experiments. Comparing with other matching systems and advanced systems of Ontology Alignment Evaluation Initiative (OAEI), the proposed method can improve the quality of matching results more effectively.

An optimized system for sensor ontology meta‐matching using swarm intelligent algorithm

“…Aifeng Geng et al 12 suggested a Multi‐Objective Particle Swarm Optimization (MOPSO) based sensor ontology mechanism to address the issues of sensor ontology heterogeneity that facilitates the fusion of two ontologies. The quality of ontology was optimized using distribution based competitive mechanism multi‐objective particle swarm algorithm (D 2 CMOPSO) and achieved qualitative matching results.…”

Heterogeneous Ontology Alignment Extraction For E-Business Through Compact Co-Evolutionary Niching Genetic Algorithm

“…More recently, the Firefly Algorithms (FAs) Xue (2020); Zhou, Lv, and Geng (2023) were used to address the meta-matching problem, using two competitive sub-populations to execute exploitation and exploration, respectively. Additionally, Particle Swarm Optimization (PSO) Geng and Lv (2023); Xue and Tsai (2022) is used to aggregate ontologies and is combined with Simulated Annealing (SA) Bertsimas and Tsitsiklis (1993) as a local search operator, reducing the number of fitness evaluations.…”

Augmented Reality and Ontology-based Approach for Enhancing Arabic Text Recognition and Visualization

“…Ontologies are employed to describe the structure and semantics of data. They aim to overcome the problems of semantic ambiguity in different domains by making them interoperable [5].…”