Fuzzy DL Research Articles

A new EEG-based schizophrenia diagnosis method through a fuzzy DL model

A new EEG-based schizophrenia diagnosis method through a fuzzy DL model

Storing fuzzy description logic ontology knowledge bases in fuzzy relational databases

In the context of the Semantic Web, fuzzy extensions to OWL (the W3C standard ontology language) and Description Logics (DLs, the logical foundation of OWL) have been extensively investigated, and there are many real fuzzy DL ontology knowledge bases. Therefore, how to store fuzzy DL ontology knowledge bases has become an important issue. In this paper, we propose an approach and implement a tool for storing fuzzy DL ontology knowledge bases in fuzzy relational databases. Our chosen formalism is a fuzzy extension of the very expressive DL SHOIN(D), which is the main logical foundation of the standard ontology language OWL, so that our storage approach can store not only fuzzy DL-knowledge bases but also fuzzy ontology knowledge bases. Firstly, we give a formal definition of fuzzy DL-knowledge bases. In the definition, we consider the constructors of both fuzzy SHOIN(D) DL and fuzzy OWL ontology and add some common fuzzy datatypes (e.g., trapezoidal values, interval values, approximate values, and labels) into the knowledge bases. On this basis, we propose an approach which can store fuzzy DL-knowledge bases in fuzzy relational databases, and provide an example to well explain the approach. The correctness and quality of the storage approach are proved and analyzed. Furthermore, following the proposed approach, we implemented a prototype tool, which can automatically store fuzzy DL-knowledge bases. Finally, we make a discussion about the query problem and make a comparison with the existing works.

Developing a fuzzy OWL ontology for obesity related cancer domain

Obesity is associated with various diseases, particularly cardiovascular diseases, diabetes type 2, obstructive sleep apnea, certain types of cancer, osteoarthritis, and asthma. The knowledge of the obesity related cancer (ORC) domain is highly required to be represented in a structured and formalised shape. In this paper, we develop an ontology to represent ORC domain knowledge with its diseases, symptoms, diagnosis, and treatments. The proposed ontology is based on the Web Ontology Language (OWL 2) integrated with the fuzzy logic. The fuzzy developed ontology handles the overlapping concepts, ingesting more concepts, and copes with the linguistic domain variables, which were not possible using the regular ontologies. It allows the users to query the fuzzy Dl reasoner for element and answer them with the fuzzy ontology. By developing the fuzzy ORC ontology, it is expected to be a good practice for the ontologists and knowledge engineers.

FOORC: A FUZZY ONTOLOGY-BASED REPRESENTATION FOR OBESITY RELATED CANCER KNOWLEDGE

Obesity has a tight relationship with increased risks of different cancer types, such as Colorectal, Ovarian, Female Breast, Gallbladder, Adenocarcinoma, Kidney (Renal-Cell), Liver, and Pancreatic. It can also lead to some other diseases like diabetes and heart diseases. This paperproposes a fuzzy ontology that is based on OWL 2to represent the Obesity Related Cancer (ORC) domain knowledge. The diseases taxonomy isconstructed using the standard Disease Ontology. The presented FuzzyOntology includes more concepts than in crisp one and copes with the domain linguistic variables. It allows the users to query the Fuzzy Dl reasoner for element and get them back the fuzzy ontology for that element. It is expected to be good practice for ontologists and knowledge engineers in medical field aiding them to solve the overlapping concepts, linguistic variables, and reasoning problems by building their fuzzy ontologies. Building FOORC as an open ontology is a first attempt to organize information related to the obesity and cancer diseases in a formalized, structured manner that both physicians and intelligent systems can exploit it in knowledge sharing, reusability, and reasoning.

Reasoning with fuzzy extensions of OWL and OWL 2

Fuzzy Description Logics (f-DLs) have been proposed as logical formalisms capable of representing and reasoning with vague/fuzzy information. They are envisioned to be helpful for many applications that need to cope with such type of information such as multimedia processing, decision making, automatic negotiation and more. Recent results have provided with many tableaux algorithms for supporting reasoning over quite expressive f-DLs. However, no (direct) tableaux algorithm for reasoning with fuzzy extensions of DLs such as $$\mathcal{SHOIQ } $$ and $$\mathcal{SROIQ } $$ exists today. $$\mathcal{SHOIQ } $$ and $$\mathcal{SROIQ } $$ are particularly interesting formalisms as they constitute the logical underpinnings of the Web ontology languages OWL DL and OWL 2 DL. In the current paper, we present an algorithm for reasoning with the fuzzy DLs f- $$\mathcal{SHOIQ } $$ and f- $$\mathcal{SROIQ } $$ . In addition, we also provide a tableaux algorithm for fuzzy nominals, thus providing reasoning support for the fuzzy DL language (we call) f- $$\mathcal{SHO }_\mathtt{{f}}\mathcal{IQ }$$ .

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.