Fuzzy OWL-Boost: Learning fuzzy concept inclusions via real-valued boosting

General Concept Inclusions in Fuzzy Description Logics

Fuzzy Description Logics (fuzzy DLs) have been proposed as a language to describe structured knowledge with vague concepts. A major theoretical and computational limitation so far is the inability to deal with General Concept Inclusions (GCIs), which is an important feature of classical DLs. In this paper, we address this issue and develop a calculus for fuzzy DLs with GCIs.

Fuzzy description logics with general concept inclusions

Learning Fuzzy Concept Definitions

A b s t r a c t --The symbolic approach to machine learning has developed algorithms for learning F i r s t O r d e r L o g i c c o n c e p t d e f i n i t i o n s . Nevertheless, most of them are limited because of their impossibility to cope with numeric features, typical of real-world data. In this paper, a method to face this problem is proposed. In particular, an extended version of the sy...

Document retrieval using fuzzy-valued concept networks

This paper presents a new method for document retrieval using fuzzy-valued concept networks, where the relevant degrees between the concepts in a fuzzy-valued concept network are represented by arbitrary shapes of fuzzy numbers. There are two kinds of relevant relationships between any two concepts in a fuzzy-valued concept network, i.e., fuzzy positive association and fuzzy negative associatio...

Fuzzy number-valued fuzzy ‎relation

It is well known fact that binary relations are generalized mathematical functions. Contrary to functions from domain to range, binary relations may assign to each element of domain two or more elements of range. Some basic operations on functions such as the inverse and composition are applicable to binary relations as well. Depending on the domain or range or both are fuzzy value fuzzy set, i...