Automatically Specifying a Parallel Composition of Matchers in Ontology Matching Process by Using Genetic Algorithm

Abstract

Today, there is a rapid increase of the available data because of advances in information and communications technology. Therefore, many mutually heterogeneous data sources that describe the same domain of interest exist. To facilitate the integration of these heterogeneous data sources, an ontology can be used as it enriches the knowledge of a data source by giving a detailed description of entities and their mutual relations within the domain of interest. Ontology matching is a key issue in integrating heterogeneous data sources described by ontologies as it eases the management of data coming from various sources. The ontology matching system consists of several basic matchers.To determine high-quality correspondences between entities of compared ontologies, the matching results of these basic matchers should be aggregated by an aggregation method. In this paper, a new weighted aggregation method for parallel composition of basic matchers based on genetic algorithm is presented. The evaluation has confirmed a high quality of the new aggregation method as this method has improved the process of matching two ontologies by obtaining higher confidence values of correctly found correspondences and thus increasing the quality of matching results.

A general approach for of schema matching problem: case of databases

“…Neural networks are used to assist the process to determine the correspondences [69] while clustering techniques are applied to reduce and limit the correspondences search space [33,70]. Genetic algorithms are used to determine some parameters like thresholds or weights used in some approaches [71,72] and to measure linguistic similarity natural language processing (NLP) are applied to labels of elements of schemas [11,73] represented by characters [74,75] or by vectors (obtained after transforming words using word embedding) [76][77][78][79]. Other techniques are employed for the data to improve the precision of the similarity computation and thus the selection of correspondences like analysis [39,80,81], statistics [82][83][84] or probabilities [85][86][87][88] that is used to make the process of matching independent as possible from parameters and human interventions.…”

Using NSGA‐III for optimising biomedical ontology alignment

“…Genetic algorithm based ontology alignment (GOAL) [22] is the first matching system that utilises EA to determine the optimal weight configuration for a weighted average aggregation of several similarity measures by considering a reference alignment. A similar idea of combining multiple similarity measures is also developed by Naya et al [23], Alexandru Lucian and Iftene [24] and Gulić et al [11]. To improve efficiency, a hybrid EA is presented to tune the parameters for aggregating various similarity measures [12, 25].…”

“…Since none of the similarity measures can distinguish the same biomedical concepts in any contexts independently, the ontology matching systems actually apply several similarity measures to determine the correspondences between particular biomedical concepts. The most common composition of multiple similarity measures is the parallel composition, where the similarity measures are executed independently from each other and the aggregated correspondence is computed afterwards [11]. Currently, researchers mainly focus on how to tune the aggregating weights for various similarity measures to improve the quality of the ontology alignments [12].…”

An experimental analysis on evolutionary ontology meta-matching

“…The OMM process usually uses (1) multiple similarity measures, (2) a method of combining similarity measures, and (3) a method of selecting candidate matches, where evolutionary algorithms are commonly employed in at least one of these last two steps. Several evolutionary algorithms have been applied, and the most common are the Genetic Algorithm (GA) [8], the Memetic Algorithm [9], and the Particle Swarm Optimization (PSO) [10].…”