Optimizing Ontology Alignments by Using Neural NSGA-II

Abstract

In this article, the authors propose a new hybrid approach based on a continuous Non-dominated Sorting Genetic Algorithm II (NSGA-II) and a neural network to refine the alignment results. This approach consists of three phases: (i) pre-alignment phase which allows to identify the formats of input ontologies, to adapt them and to transform them into Ontology Web Language (OWL) in order to solve the problem of heterogeneity of representation. (ii) alignment phase which combines syntactic and linguistic matching techniques and methods, based on the relevant attributes per different points of syntactic and structural technic. (iii) The post-alignment phase which optimizes the matching by a hybrid technique of continuous NSGA-II and networks of neurons. This approach is compared with the greatest systems per the Ontology Alignment Evaluation Initiative (OAEI) standard. The experimental results appear that the proposed approach is effective.

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.…”

An experimental analysis on evolutionary ontology meta-matching

“…In some articles that present new proposals for OMM, the evaluation methods focus on accuracy results and do not explore relevant characteristics of the algorithms used. In [10,31,32], different OMM approaches are proposed, and the evaluations are made in comparing Precision, Recall and F-measure values. These three metrics are sufficient to validate whether the experiment is capable of finding solutions to the problem, but they are insufficient, when applied in completely different experiments, to answer which part of the approach is responsible for the results.…”

“…This section is dedicated to reporting the definition of each hypothesis tested in this work, as well as presenting the motivations and justifications behind each hypothesis. The first hypothesis concerns the use of multiple similarity measures and is defined as: H1 To use String-based, Taxonomy-based and Linguistic Resources similarity measures is enough to create a resilient tool Although similarity measures represent the basis for starting the experiment, most articles do not include similarity measures in their evaluation methodologies; they only describe which measures were used and how they work, as in [9,31,34]. It is difficult to determine which measures contributed to a particular alignment.…”

A framework to aggregate multiple ontology matchers

“…There are approaches to define weights for similarity functions, as reviewed in Otero-Cerdeira et al (2015) and Souza et al (2014). Recent approaches were developed by Xue et al (2018a), Biniz and El Ayachi (2018), Xue and Wang (2017) and Xue and Liu (2017), which present a genetic algorithm used to solve multi-objective problems. The authors use multiobjective evolutionary algorithms, such as non-dominated sorting genetic algorithm-II (Deb et al, 2000) and multiobjective evolutionary algorithm based on decomposition (Zhang and Li, 2007), to solve the problem of OMM and demonstrate that the algorithm is able to generate good results on OAEI data set.…”