Aggregated Similarity Optimization in Ontology Alignment through Multiobjective Particle Swarm Optimization

Abstract

Abstract:The basic idea behind the ontology is to conceptualize information that is published in electronic format. The problem of ontology alignment is defined as identifying the relationship shared by the set of different entities where each entity belongs to separate ontology. The amount of similarity between two entities from two different ontologies takes part into the ontology alignment process. There are several similarity measuring methods available in the existing literature for measuring the similarity between two discrete entities from different ontologies. To obtain a comprehensive and precise result, all the similarity measures are integrated. One of the ways to combine the various similarity measures is weight-based similarity aggregation. Usually the weights with respect to various similarity measures are assigned manually or through some method. But most of the existing techniques suffer from lack of optimality. Also many evolutionary based approaches are available to find the optimal solution for weight-based similarity aggregation but they are designed as single objective optimization problem. This fact has inspired us to develop a multiobjective particle swarm based optimization algorithm for generating optimal weight based similarity aggregation to get a optimal alignment. In this article, two objectives precision and recall are simultaneously optimized. Moreover a local search is conducted for replacing the worst population in the new generation by best population acquired from the history. The proposed study is evaluated using an artificial data set and performance of the proposed method is compared with that of its single objective versions.

A framework to aggregate multiple ontology matchers

“…Further studies should include new approaches for the calibration of weights of the matchers. The GNOSISþ implements an uni-objective approach; however, multiobjective approaches can be used to adapt to user's need when the user wishes to balance both precision and recall or has other restrictions which may influence the weight of the matchers, such as maximize the amount of correspondences or minimize the amount of matchers through performance restrictions, such as Xue and Wang (2017) and Marjit (2015). Therefore, probability approaches (Kimmig et al, 2017;Jiménez-Ruiz et al, 2016) can make the calibrator module less dependent on the initial input data.…”

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

“…Table 1 shows the shortcomings of diffenrent RA and PRA-based matching systems. In the process of ontology matching, the RA-based matching method compare the solution with the reference alignment, and these systems are mainly found in the literature [25][26][27]46] and [4]. Although it can improve the precision of the matching result to some extent, but it is not reasonable: because it is time & labor-consuming to build the reference alignment in practice.…”

“…ECOMatch [33] also requires the user to provide a part of matching elements, on the basis of which the system parameters are set and the ontology matching process is further completed. However, Table 1 Reference alignment and partial reference alignment-based matching systems and their shortcomings Name Shortcomings GOAL [26] It requires the reference matching result to be given in advance Martinez and Aldana [27] It needs the help of reference alignments Xue et al [46], Marjit [25], Biniz and El Ayachi [4] None of these methods escape the constraints of reference alignment SAMBO [20], LSD [10], ECOMatch [33] These methods require continuous user participation and the selected representative entities does not accurately represent the original ontology Xue et al [47] It is hard to choose a suitable set of small-scale matching pairs to represent the original ontology these methods require continuous user participation and the selected representative entities does not accurately represent the original ontology. For this reason, Xue et al [47] propose a PRA-based system using clustering method, where entities in the ontology are divided into different clusters, and entities that can maximize the representation of the original ontology are selected from these clusters.…”

“…However, either of them ignore the structural information of entities in the ontology and lack the structure-based similarity measure. In addition, Marjit [25] also propose an MOPSO-based ontology meta-matching method earlier. Moreover, Kureychik and Semenova [19] use MOPSO to address the application of ontology metamatching in large-scale data processing.…”

An experimental analysis on evolutionary ontology meta-matching

“…The H2 hypothesis comes precisely to verify the applicability of the NCD. There are several works addressing the OMM problem using different meta-heuristics, such as Genetic Algorithm (GA) [21], Memetic Algorithm [36], and Particle Swarm Optimization (PSO) [37]. Each meta-heuristic can make use of one or more objective functions, which are responsible for guiding the meta-heuristic solution(s) in the search space.…”