Granular computing applied to ontologies

Abstract

Granular Computing is an emerging conceptual and computing paradigm of information processing. A central notion is an information-processing pyramid with different levels of clarifications. Each level is usually represented by 'chunks' of data or granules, also known as information granules. Rough Set Theory is one of the most widely used methodologies for handling or defining granules. Ontologies are used to represent the knowledge of a domain for specific applications. A challenge is to define semantic knowledge at different levels of human-depending detail. In this paper we prose four operations in order to have several granular perspectives for a specific ontological commitment. Then these operations are used to have various views of an ontology built with a rough-set approach. In particular, a rough methodology is introduced to construct a specific granular view of an ontology.

Formal concept analysis based on fuzzy granularity base for different granulations

“…(1568, 5 ) i (158, 9 5 ) n (58, 9 4 5 ) e (134578, 9 ) j (578, 9 4 ) o (5, 9 3 Table 6 Specific interpretation of all the nodes in Fig. 3.…”

“…effective tool with vast potential for knowledge acquisition, GrC has been widely investigated by researchers in the field of artificial intelligence [5,15,17,18,20,21,24]. In addition, the fuzzy equivalence relation [30] of fuzzy set theory is introduced into FCA, and we propose a model of FCA based on the fuzzy equivalence for different granulations.…”

Decision making with a fuzzy ontology

“…Thus, the idea to build knowledge mobilisation tools that can turn amateurs into wine connoisseurs was born. An ontology of wines seems to be a good way to show the potential of fuzzy ontology for expressing concepts (Calegari andCiucci 2006, 2010).…”

Generalization of Pawlak’s rough approximation spaces by using δβ-open sets

“…It has been successfully applied such artificial intelligence fields as machine learning, pattern recognition, decision analysis, cognitive sciences, intelligent decision making and process control [6,[13][14][15]19,32,41,44]. Some of rough set applications are to approximate an arbitrary a universe by two definable subsets called lower and upper approximations, and to reduce the number of the set of attributes in data sets.…”