No abstract

No abstract

Ontologies are recognized as a fundamental component for enabling interoperability across heterogeneous systems and applications. Indeed, they try to fit a common understanding of concepts in a particular domain of interest to support the exchange of information among people, artificial agents, and distributed applications. Unfortunately, because of human subjectivity, various ontologies related to the same application domain may use different terms for the same meaning or may use the same term to mean different things, raising the so-called heterogeneity problem. The ontology alignment process tries to solve this semantic gap by individuating a collection of similar entities belonging to different ontologies and enabling a full comprehension among different actors involved in a given knowledge exchanging. However, the complexity of the alignment task, especially for large ontologies, requires an automated and effective support for computing high-quality alignments. The aim of this paper is to propose a memetic algorithm to perform an efficient matching process capable of computing a suboptimal alignment between two ontologies. As shown by experiments, the memetic approach is more suitable for ontology alignment problem than a classical evolutionary technique such as genetic algorithms. © 2012 Wiley Periodicals, Inc

Ontologies have been frequently employed in order to solve problems derived from the management of shared distributed knowledge and the efficient integration of information across different applications. However, the process of ontology building is still a lengthy and error-prone task. Therefore, a number of research studies to (semi-)automatically build ontologies from existing documents have been developed. In this paper, we present our approach to extract relevant ontology concepts and their relationships from a knowledge base of heterogeneous text documents. We also show the architecture of the implemented system and discuss the experiments in a real-world context