Nowadays, ontology as a knowledge sharing approach plays an important role in semantic interoperability of enterprise applications (EAs). However, the manual process of ontology construction requires deep understanding of the domain. This approach is difficult, expensive and time-consuming. To overcome the knowledge acquisition bottleneck, ontology learning field aims to provide automatic and semi-automatic approaches for ontology generation. Several approaches have been emerged for this purpose. In this paper, we present a practical study of methods that take data models as input to the learning process. The main contributions of this work are: (i) the evaluation of the availability of existing approaches for (semi-)automatic generation of ontology from data models; (ii) the evaluation of tools according to their operability; and (iii) the evaluation of the resulting ontologies to assess their quality in supporting semantic interoperability. Our goal through this study is to find a response to the question: Is there a tool that extracts (semi-)automatically an application ontology from data models, intended for use in semantic interoperability?.
Abstract. Developing ontology for modeling the universe of a Relational Database (RDB) is a key success for many RDB related domains, including semantic-query of RDB, Linked Data and semantic interoperability of information systems. However, the manual development of ontology is a tedious task, error-prone and requires much time. The research field of ontology learning aims to provide (semi-) automatic approaches for building ontology. However, one big challenge in the automatic transformation, is how to label the relationships between concepts. This challenge depends heavily on the correct extraction of the relationship types. In fact, the RDB model does not store the meaning of relationships between entities, it only indicates the existence of a link between them. This paper suggests a solution consisting of a meta-model for the semantic enrichment of the RDB model and of a classification of relationships. A case study shows the effectiveness of our approach.
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