A Prey–Predator Approach for Ontology Meta-matching

Ferranti, Nicolas; Souza, Jairo; Soares, Stênio Sã Rosário Furtado

doi:10.1007/s13740-021-00125-y

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…GA is based on the classical algorithm proposed by Holland [20] inspired by the evolutionary biology and implementation details can be found in [21]. PPA algorithm is based on the movement pressure that forces a set of preys (average solutions) to run away from a predator (worst solution); this algorithm was based on the approach proposed by [22] and implementation details can be found in [23]. Both GA and PPA are approaches for global optimization, to check the behavior of local search metaheuristics this research also evaluated a GRASP-based approach.…”

Section: Popular Methods Used In Omm Approachesmentioning

confidence: 99%

An experimental analysis on evolutionary ontology meta-matching

2021

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Every year, new ontology matching approaches have been published to address the heterogeneity problem in ontologies. It is well known that no one is able to stand out from others in all aspects. An ontology meta-matcher combines different alignment techniques to explore various aspects of heterogeneity to avoid the alignment performance being restricted to some ontology characteristics. The meta-matching process consists of several stages of execution, and sometimes the contribution/cost of each algorithm is not clear when evaluating an approach. This article presents the evaluation of solutions commonly used in the literature in order to provide more knowledge about the ontology meta-matching problem. Results showed that the more characteristics of the entities that can be captured by similarity measures set, the greater the accuracy of the model. It was also possible to observe the good performance and accuracy of local search-based meta-heuristics when compared to global optimization meta-heuristics. Experiments with different objective functions have shown that semi-supervised methods can shorten the execution time of the experiment but, on the other hand, bring more instability to the result.

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Section: Popular Methods Used In Omm Approachesmentioning

confidence: 99%

An experimental analysis on evolutionary ontology meta-matching

2021

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“…Ontology matching is typically framed as the task of determining exact weights and thresholds to obtain high quality alignments. 9,10 The research aims to enhance semantic interoperability among intelligence systems by matching sensor ontologies, using a method called Simulated Annealing-Beetle Swarm Optimization Algorithm. 11 This matching is crucial for IoT, smart cities, and sensor-based applications, improving interoperability and semantic understanding among sensors and systems to enhance ontology alignments for sensor data quality.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is common practice to aggregate several distinct similarity measures to enhance the confidence of the results. Ontology matching is typically framed as the task of determining exact weights and thresholds to obtain high quality alignments 9,10 . The research aims to enhance semantic interoperability among intelligence systems by matching sensor ontologies, using a method called Simulated Annealing‐Beetle Swarm Optimization Algorithm 11 .…”

Section: Introductionmentioning

confidence: 99%

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

Lateef Haroon P S,

Patil,

Bidare Divakarachari

et al. 2024

Internet Technology Letters

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It is beneficial to annotate sensor data with distinct sensor ontologies in order to facilitate interoperability among different sensor systems. However, for this interoperability to be possible, comparable sensor ontologies are required since it is essential to make meaningful links between relevant sensor data. Swarm Intelligent Algorithms (SIAs), namely the Beetle Swarm Optimisation Algorithm (BSO), present a possible answer to ontology matching problems. This research focuses on a method for optimizing ontology alignment that employs BSO. A novel method for effectively controlling memory use and striking a balance between algorithm exploration and exploitation is proposed: the Simulated Annealing‐based Beetle Swarm Optimisation Algorithm (SA‐BSO). Utilizing Gray code for solution encoding, two compact operators for exploitation and exploration, and Probability Vectors (PVs) for swarming choosing exploitation and exploration, SA‐BSO combines simulated annealing with the beetle search process. Through inter‐swarm communication in every generation, SA‐BSO improves search efficiency in addressing sensor ontology matching. Three pairs of real sensor ontologies and the Conference track were used in the study to assess SA‐BSO's efficacy. Statistics show that SA‐BSO‐based ontology matching successfully aligns sensor ontologies and other general ontologies, particularly in conference planning scenarios.

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On the Convergence of Prey Predator Algorithm

Tilahun

2024

Int. J. Appl. Comput. Math

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A Prey–Predator Approach for Ontology Meta-matching

Cited by 3 publications

References 17 publications

An experimental analysis on evolutionary ontology meta-matching

An experimental analysis on evolutionary ontology meta-matching

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

On the Convergence of Prey Predator Algorithm

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