Ontology Mapping based on Similarity Measure and Fuzzy Logic

In this paper, we present a method of an ontology mapping based on a similarity measure and Fuzzy logic in order to classify (i) the similarity of the ontology structure of learning object repositories and (ii) LOR which stores metadata of learning objects based on our ontology model. In this model, values of the ontology similarity are computed for concepts, properties, and relations. The ontology similarity uses parameters based on the Fuzzy Control Language (FCL) which consists of a fuzzy set of the ontology similarity (“Less”, “Same”, “More”), 7 classes of ontology similarity, and rules of the classification of ontologies. The formula of similarity measure by the Jaccard’s coefficient is applied to map a similarity of ontology structures. At the end of the article, we show an experience of implementation this model as a prototype. Introduction Nowadays, ontologies are applied to various domains such as e-learning where they are used for describing metadata of learning resources to help searching and retrieving learning objects in their repository. However the usage of ontology structures may be different in some learning object repositories and this leads to the problem of searching learning objects in various learning object repositories. To solve this problem, we propose a model to define and classify an ontology similarity, which is meant the similarity of ontology structures, in order to select suitable learning object repositories for the searching and retrieving learning objects in these repositories. Our Learning Object Repository (LOR) is a system that stores learning objects on the Web and/or their metadata for the serving of searching and retrieving learning objects on the Internet and the structure of metadata is based on ontology model. In this paper, we apply a similarity measure for ontology mapping in order to compute the probability of the similarity of ontology structure and Fuzzy Logic is used for classifying the ontology similarity. The technique of ontology mapping in our system is implemented by (1) the comparison of 3 main categories of ontology structure: concepts, properties, and relations, (2) the computation of the probability of the similarity measure, and (3) the classification of ontology similarity with Fuzzy Logic. Definitions and purposes of an ontology Gruber (1993) defines an ontology as “a specification of a conceptualization”. Another definition is given in (Studer et al, 1998): “An ontology is a formal, explicit specification of a shared conceptualization”. The specification of the conceptualization consists of the objects, concepts and other entities that are in the same particular domain and the relationships that hold among them. “Explicit” means that objects, concepts, and other entities are explicitly defined. “Formal” implies that the ontology should be machine readable. “Shared” means that the ontology captures consensual knowledge and is agreed-upon by a group, not just an individual. In generally, the main structure of ontology model consists of tree main categories: concept or class, property, and relation. In learning resource management, ontology has been applied to improve the structure and the usefulness of Learning Design Repository in IDLD (Implement and Development of the Learning Design) project (see Paquette et al, 2006). The use of the ontology could be employed as an approach to implement a semantic Web-based e-learning system. This framework is focused on the RDF (Resource Description Framework) data model, OWL ontology language and RAP for parsing RDF documents (Fayed et al, 2006). Recently, research on the ontology technology aims at the act of interoperability and reusability. It means that similar objects which are described in different ontology structures could be integrated into a new ontology structure and they could be utilized in a particular system. This technology is known as an ontology mapping. As described in (Laurel et al, 2004), there are two types of ontology mapping: source-based and instance-based. Examples of source-based mapping tools are PROMPT, Chimaera, and ONION and examples of instance-based mapping tools are FCA-Merge and GLUE. Beyond, a new methodology for merging the heterogeneous domain ontologies based on the WordNet which is used as a dictionary to give relationships between concepts detailed in (Kong et al, 2005). Defining and classifying the ontology similarity In this section, we present how to define and classify the ontology similarity. The manner of the similarity measure is used for defining the ontology similarity while the Fuzzy Logic model is applied to classify the ontology similarity. Similarity measure of ontologies Similarity measure can define a similarity of any two ontologies involved. The well-known formula of similarity measure is Jaccard’s coefficient which appears in (Doan et al, 2002) as shown following: