The use of ontologies for representing database schemas of fuzzy information

Abstract

In this paper, an ontology system is proposed to represent the knowledge structure enabling fuzzy information to be stored in fuzzy databases. This proposal allows users or applications to simplify the metadata definition process that is necessary for representing and managing imprecise and classic information in these databases. This ontology then acts as an interface that formalizes the representation of such structures and allows access to them. The instances obtained from this ontology represent the schemas that describe domain information in a database. The description of fuzzy and classic database schemas allows access to online public databases for which no other semantic description is associated. This paper also presents another ontology to represent these schemas as instances. Not only does this ontology allow fuzzy data values to be stored (because of the definition of fuzzy data types as classes of the ontology) but it also enables schema tables and attributes to be defined. C 2008 Wiley Periodicals, Inc.

A Framework for Fuzzy Ontology Storing onto Relational Database within an a Priori Data Integration System

“…In [20] and [21], an ontology system is proposed to represent the knowledge structure enabling fuzzy information to be stored in fuzzy databases. Instances of ontology system represent diagrams describing the domain information in a database.…”

Towards integrating fuzzy logic capabilities into an ontology-based Inductive Logic Programming framework

“…We have decided to make this contribution publicly available 5 to be used as a common test case for testing this kind of reasoners. …”

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Database Marketing Process Supported by Ontologies: a Data Mining System Architecture Proposal

“…Nevertheless, ontological development is mainly dedicated to a community (e.g., genetics, cancer or networks) and, therefore, is almost unavailable to others outside it. Indeed the new knowledge produced from reused and shared ontologies is still very limited (Guarino, 1998) (Blanco et al, 2008) (Coulet et al, 2008) (Sharma and Osei-Bryson, 2008) (Cardoso and Lytras, 2009). To the best of our knowledge, in spite of successful ontology approaches to solve some KDD related problems, such as, algorithms optimization (Kopanas et al, 2002) (Nogueira et al, 2007), data pre-processing tasks definition (Bouquet et al, 2002) (Zairate et al, 2006) or data mining evaluation models (Cannataro and Comito, 2003) (Brezany et al, 2008), the research to the ontological KDD process assistance is sparse and spare.…”

“…scalability. Yet, it is obvious that the Semantic Web will never become a reality if ontologies cannot be developed to the point of functionality, availability and reliability comparable to the existing components of the Web (Blanco et al, 2008) (Cardoso and Lytras, 2009 Making explicit domain assumptions underlying an implementation makes it possible to change these programming-language codes making these assumptions not only hard to find and understand but also hard to change, in particular for someone without programming expertise. In addition, explicit specifications of domain knowledge are useful for new users who must learn what terms in the domain mean; -Separating the domain knowledge from the operational knowledge is another common use of ontologies, e.g., regarding computers hardware components, it is possible to describe a task of configuring a product from its components according to a required specification and implement a program that does this configuration independent of the products and components themselves.…”