A framework to aggregate multiple ontology matchers

Abstract

Purpose Although ontology matchers are annually proposed to address different aspects of the semantic heterogeneity problem, finding the most suitable alignment approach is still an issue. This study aims to propose a computational solution for ontology meta-matching (OMM) and a framework designed for developers to make use of alignment techniques in their applications. Design/methodology/approach The framework includes some similarity functions that can be chosen by developers and then, automatically, set weights for each function to obtain better alignments. To evaluate the framework, several simulations were performed with a data set from the Ontology Alignment Evaluation Initiative. Simple similarity functions were used, rather than aligners known in the literature, to demonstrate that the results would be more influenced by the proposed meta-alignment approach than the functions used. Findings The results showed that the framework is able to adapt to different test cases. The approach achieved better results when compared with existing ontology meta-matchers. Originality/value Although approaches for OMM have been proposed, it is not easy to use them during software development. On the other hand, this work presents a framework that can be used by developers to align ontologies. New ontology matchers can be added and the framework is extensible to new methods. Moreover, this work presents a novel OMM approach modeled as a linear equation system which can be easily computed.

An experimental analysis on evolutionary ontology meta-matching

“…This research evaluated three different metaheuristics: Genetic Algorithm (GA), Prey-Predator Algorithm (PPA), and a Greedy Randomized Adaptive Search Procedure (GRASP). 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].…”

“…It is a harmonic mean between MatchRatio and MatchCoverage, where MatchRatio and MatchCoverage are substitutes for Precision and Recall, respectively [25]. -Linear System-based objective function evaluates the ability of a solution to solve a linear system built from a set of reference correspondences [21]. It is considered a semisupervised function due to the size of the reference set, which is from 3 to 4% of the correct alignment.…”

“…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.…”

“…Figure 2 presents a comparison between the number of supervised/semi-supervised and unsupervised approaches in the literature, pointing to an increase in the number of unsupervised in recent years. In the Optimization step, both supervised/semi-supervised [21] and unsupervised [36] approaches can be used. Both [21,36] evaluate the research in the OAEI bibliobenchmark, and the authors applied meta-heuristics in the optimization step, but with different objective functions.…”

“…In the Optimization step, both supervised/semi-supervised [21] and unsupervised [36] approaches can be used. Both [21,36] evaluate the research in the OAEI bibliobenchmark, and the authors applied meta-heuristics in the optimization step, but with different objective functions. The evaluation of [21,36] is done in terms of the F-measure; [36] still includes comparisons of time and memory spent.…”