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A Granular Computing Method for OWL Ontologies

Lisi F. A., Mencar C.

Fundamenta Informaticae

2018

Vol. 159, nr 1/2

147--174

We propose a method to extract and integrate fuzzy information granules from a populated OWL ontology. The purpose of this approach is to represent imprecise knowledge within an OWL ontology, as motivated by the fact that the Semantic Web is full of imprecise and uncertain information coming from perceptual data, incomplete data, data with errors, etc. In particular, we focus on Fuzzy Set Theory as a means for representing and processing information granules corresponding to imprecise concepts usually expressed by linguistic terms. The method applies to numerical data properties. The values of a property are first clustered to form a collection of fuzzy sets. Then, for each fuzzy set, the relative σ-count is computed and compared with a number of predefined fuzzy quantifiers, which are therefore used to define new assertions that are added to the original ontology. In this way, the extended ontology provides both a punctual view and a granular view of individuals w.r.t. the selected property. We use a real-world ontology concerning hotels and populated with data of the Italian city of Pisa, to illustrate the method and to test its implementation. We show that it is possible to extract granular properties that can be described in natural language and smoothly integrated in the original ontology by means of annotated assertions.

Generation of interpretable fuzzy granules by a double-clustering technique

Castellano G., Fanelli A. M., Mencar C.

Archives of Control Sciences

2002

Vol. 12, no. 4

397-410

This paper proposes an approach to derive fuzzy granules from numerical data. Granules are first formed by means of a double-clustering technique, and then properly fuzzyfied so as to obtain interpretable granules, in the sense that they can be described by linquistic labels. The double-clustering technique involves two steps. First, information granules are induced in the space of numerical data via the FCM algorithm. In the second step, the prototypes obtained in the first step are further clustered along each dimension via a hierarchical clustering, in order to obtain one-dimensional granules that are afterwards quantified as fuzzy sets. The derived fuzzy sets can be used as building blocks of fuzzy rule-based model. The approach is illustrated with the aid of a benchmark classification example that provides insight into the interpretability of the induced granules and their effect on the results of classification.