Fuzzy OWL Research Articles

Incorporation of Fuzzy Theory with Heavyweight Ontology and Its Application on Vague Information Retrieval for Decision Making

The decision making process is based on accurate and timely available information. To obtain precise information from the internet is becoming more difficult due to the continuous increase in vagueness and uncertainty from online information resources. This also poses a problem for blind people who desire the full use from online resources available to other users for decision making in their daily life. Ontology is considered as one of the emerging technology of knowledge representation and information sharing today. Fuzzy logic is a very popular technique of artificial intelligence which deals with imprecision and uncertainty. The classical ontology can deal ideally with crisp data but cannot give sufficient support to handle the imprecise data or information. In this paper, we incorporate fuzzy logic with heavyweight ontology to solve the imprecise information extraction problem from heterogeneous misty sources. Fuzzy ontology consists of fuzzy rules, fuzzy classes and their properties with axioms. We use Fuzzy OWL plug-in of Protege to model the fuzzy ontology. A prototype is developed which is based on OWL-2 (Web Ontology Language-2), PAL (Protege Axiom Language), and fuzzy logic in order to examine the effectiveness of the proposed system.

Fuzzy extensions of OWL: Logical properties and reduction to fuzzy description logics

The Semantic Web is an extension of the current web, where information would have precisely defined meaning, based on knowledge representation languages. The current W3C standard for representing knowledge is the Web Ontology Language (OWL). OWL is based on Description Logics which is a popular knowledge representation formalism. Although, DLs are quire expressive they feature limitations with respect to what can be said about vague knowledge, which appears in several applications. Consequently, fuzzy extensions to OWL and DLs have gained considerable attention. In the current paper we study fuzzy extensions of the Semantic Web language OWL. First, we present the (abstract) syntax and semantics of a rather elementary fuzzy extension of OWL creating fuzzy OWL (f-OWL). More importantly we use this extension to provide an investigation on the semantics of several f-OWL axioms and more precisely for those which, in classical DLs, can be expressed in different but equivalent ways. Moreover, we present a translation method which reduces inference problems of f-OWL into inference problems of expressive fuzzy Description Logics, in order to provide reasoning support through fuzzy DLs. Finally, we present two further fuzzy extensions of OWL based on fuzzy subsumption and fuzzy nominals.

Formal semantics-preserving translation from fuzzy ER model to fuzzy OWL DL ontology

Ontology is an important part of the W3C standards for the Semantic Web, and how to quickly and cheaply construct Web ontologies has become a key technology to enable the Semantic Web. However, information imprecision and uncertainty exist in many real-world applications, thus constructing fuzzy ontology by extracting domain knowledge from fuzzy database models (e.g., fuzzy ER model) can profitably support fuzzy ontology development. In this paper, we propose an approach for constructing fuzzy ontology from fuzzy ER model, in which the fuzzy ontology consists of fuzzy ontology structure and instances. Firstly, we give the formal definition and the semantics of fuzzy ER models. Then, we introduce the fuzzy extension of ontology language OWL DL, i.e., fuzzy OWL DL. Based on the fuzzy OWL DL, a kind of fuzzy ontology called fuzzy OWL DL ontology is presented. Furthermore, we consider the fuzzy ER schema and the corresponding database instances, and translate them into the fuzzy ontology structure and the fuzzy ontology instances, respectively. Finally, since a fuzzy OWL DL ontology is equivalent to a fuzzy Description Logic f-SHOIN(D) knowledge base, how the reasoning problems of fuzzy ER models (e.g., satisfiability, subsumption, and redundancy) may be reduced to reasoning on f-SHOIN(D) knowledge bases is investigated, which will further contribute to constructing fuzzy OWL DL ontology that exactly meet application's needs. Of course, the correctness of the translation and reasoning problems are proved completely.

Uncertainty and the Semantic Web

The semantic Web must handle information from applications that have special knowledge representation needs and that face uncertain, imprecise knowledge. More precisely, some applications deal with random information and events, others deal with imprecise and fuzzy knowledge, and still others deal with missing or distorted information - resulting in uncertainty. To deal with uncertainty in the semantic Web and its applications, many researchers have proposed extending OWL and the description logic (DL) formalisms with special mathematical frameworks. Researchers have proposed probabilistic, possibilistic, and fuzzy extensions, among others. Researchers have studied fuzzy extensions most extensively, providing impressive results on semantics, reasoning algorithms, and implementations. Building on these results, we've created a fuzzy extension to OWL called Fuzzy OWL. Fuzzy OWL can capture imprecise and vague knowledge. Moreover, our reasoning platform, fuzzy reasoning engine (FiRE), lets Fuzzy OWL capture and reason about such knowledge