The integration of Linked Data in knowledge-based systems (KBS) has the potential to reduce the maintenance effort for the underlying knowledge base. In simple terms, the Web of (Linked) Data is a distributed database with semantic facts about the world or any specific domain. This integration allows a collaborative system’s users to exchange resources in a optimally way, using its own concepts as a filter to execute any tasks in the collaborative environment. Based on these aspects, this paper proposes ontological alignment method that allow integrate semantically different data and information repositories through an ontological layer, used to data conceptualization. In order to provide a more easily and efficient way to found and share those data more efficiently, those repositories must be integrated in a single uniform format, automatically, and negotiated safe. The information and resources produced in these collaborative environments are heterogeneous in essence, and stored in different kinds of data repositories. These have provided the rise of social networks, which operate with information and resource sharing tools. These interactions produce a large amount of data and information. This interaction occurs, mainly, by social relationships in different levels, and the multimedia resource’s search and sharing. The increasing demand for new technologies allow greater interactivity and participation of people in online environments.
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The approach proposed here is supported by an ad hoc software framework whose scope is easing the creation of new ontologies by promoting the reuse of existing ones and automatizing, as much as possible, the whole ontology construction procedure. Since ontologies are the de facto standard in representing and sharing knowledge models over the web, this paper presents a comprehensive methodology to ontology integration and reuse based on various matching techniques. As more and more outbreaks of this new revolution light up, a major challenge came soon into sight: addressing the main objectives of the semantic web, the sharing and reuse of data, demands effective and efficient methodologies to mediate between models speaking different languages. This revolution, together with the increasing digitization of the world, has led to a high availability of knowledge models, i.e., more or less formal representations of concepts underlying a certain universe of discourse, which span throughout a wide range of topics, fields of study and applications, mostly heterogeneous from each other at a different dimensions. The new revolutionary web today, the Semantic Web, has augmented the previous one by promoting common data formats and exchange protocols in order to provide a framework that allows data to be shared and reused across application, enterprise, and community boundaries. What is more, it is less sensitive to the subjective evaluations done by experts. However, the developed method allows reducing the time for preparing an ontology to the integration process. The attribute semantics designated by human experts and based on WordNet does not affect the integration result. For this purpose, we have proposed a semi-automatic method for defining attribute semantics based on WordNet. In other words, we need to assign semantics for each attribute within a concept.
In our research, we focus on concepts level where attributes must be interpretable because they are the primary carriers of the meaning of the entire ontology. A typical ontology consists of concepts with attributes, relations between them, and instances. The goal of this paper is to address some still unresolved problems related to providing meanings in ontologies. So far, ontology developers have been forced to provide the semantics of modeled objects and relations between them manually. Thus, ontologies not only structure raw data, but also contain the meaning of those data. Ontologies are built to store and process knowledge about objects and dependencies between them. By ontology, we understand a knowledge structure which well reflects the complexity of a real world.
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