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.
Highlights
The amount of available data has increased rapidly due to advances in information and communications technology
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
alignment between two ontologies (An) ontology enriches the knowledge of a data source by giving a detailed description of entities and their mutual relations within the domain of interest
Summary
The amount of available data has increased rapidly due to advances in information and communications technology. There exist many data sources that describe the same domain of interest. These data sources are usually designed independently of each other and are mutually heterogeneous. At some later point, such heterogeneous data sources describing the same domain of interest frequently need to be coupled. An ontology enriches the knowledge of a data source by giving a detailed description of entities and their mutual relations within the domain of interest. The use of ontologies facilitates the integration of heterogeneous data sources that belong to the same domain [1].
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