Metaheuristics-based ontology meta-matching approaches

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

doi:10.1016/j.eswa.2021.114578

Cited by 18 publications

(12 citation statements)

References 46 publications

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“…Optimization is the step responsible for combining different similarity measures and define the final confidence score for a candidate match. In this step, the literature shows that evolutionary metaheuristics are the most common algorithms used to execute this combination [19]. This research evaluated three different metaheuristics: Genetic Algorithm (GA), Prey-Predator Algorithm (PPA), and a Greedy Randomized Adaptive Search Procedure (GRASP).…”

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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“…To combine these matchers, we assign the weights for their corresponding similarity matrices and then aggregate these matrices into the final one. e sensor ontology meta-matching problem investigates how to find an optimal weight set to determine a highquality alignment [26]. Here, we model the sensor ontology metamatching problem as a singleobjective optimization problem, which takes maximizing the alignment's quality as the objective.…”

Section: Sensor Ontology Meta-matching Problemmentioning

confidence: 99%

Solving Sensor Ontology Metamatching Problem with Compact Flower Pollination Algorithm

Lian

Niu

et al. 2022

Wireless Communications and Mobile Computing

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To implement co-operation among applications on the Internet of Things (IoT), we need to describe the meaning of diverse sensor data with the sensor ontology. However, there exists a heterogeneity issue among different sensor ontologies, which hampers their communications. Sensor ontology matching is a feasible solution to this problem, which is able to map the identical ontology entity pairs. This work investigates the sensor ontology meta-matching problem, which indirectly optimizes the sensor ontology alignment’s quality by tuning the weights to aggregate different ontology matchers. Due to the largescale entity and their complex semantic relationships, swarm intelligence (SI) based techniques are emerging as a popular approach to optimize the sensor ontology alignment. Inspired by the success of the flower pollination algorithm (FPA) in the IoT domain, this work further proposes a compact FPA (CFPA), which introduces the compact encoding mechanism to improve the algorithm’s efficiency, and on this basis, the compact exploration and exploitation operators are proposed, and an adaptive switching probability is presented to trade-off these two searching strategies. The experiment uses the ontology alignment evaluation initiative (OAEI)’s benchmark and the real sensor ontologies to test CFPA’s performance. The statistical comparisons show that CFPA significantly outperforms other state-of-the-art sensor ontology matching techniques.

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“…These studies aim to identify all relevant evidence on the topic/area or research question. Up to now, many such studies were published in Computer Science journals or conferences [48][49][50][51]. They are found to be useful for educational purposes as well, providing a good starting point for a PhD work [52].…”

Section: Systematic Reviewmentioning

confidence: 99%

Attraction Basins in Metaheuristics: A Systematic Mapping Study

2021

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Context: In this study, we report on a Systematic Mapping Study (SMS) for attraction basins in the domain of metaheuristics. Objective: To identify research trends, potential issues, and proposed solutions on attraction basins in the field of metaheuristics. Research goals were inspired by the previous paper, published in 2021, where attraction basins were used to measure exploration and exploitation. Method: We conducted the SMS in the following steps: Defining research questions, conducting the search in the ISI Web of Science and Scopus databases, full-text screening, iterative forward and backward snowballing (with ongoing full-text screening), classifying, and data extraction. Results: Attraction basins within discrete domains are understood far better than those within continuous domains. Attraction basins on dynamic problems have hardly been investigated. Multi-objective problems are investigated poorly in both domains, although slightly more often within a continuous domain. There is a lack of parallel and scalable algorithms to compute attraction basins and a general framework that would unite all different definitions/implementations used for attraction basins. Conclusions: Findings regarding attraction basins in the field of metaheuristics reveal that the concept alone is poorly exploited, as well as identify open issues where researchers may improve their research.

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Metaheuristics-based ontology meta-matching approaches

Cited by 18 publications

References 46 publications

An experimental analysis on evolutionary ontology meta-matching

An experimental analysis on evolutionary ontology meta-matching

Solving Sensor Ontology Metamatching Problem with Compact Flower Pollination Algorithm

Attraction Basins in Metaheuristics: A Systematic Mapping Study

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