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A Semi-automatic Method to Fuzzy-Ontology Design by using Clustering and Formal Concept Analysis

Authors:
Amira Aloui
Alaa Ayadi
Amel Grissa-Touzi

Keywords: Data Mining, Clustering, Formal Concept Analysis, Fuzzy Logic, Ontology, Fuzzy OWL2.

Abstract:
Abstract—Ontology design is a complex and time-consuming process. It is extremely difficult for experts to discover ontology from given data or texts. This paper presents a semi-automatic method for Fuzzy Ontology extraction and Design (FOD). The method is based on conceptual clustering, fuzzy logic, and Formal Concept Analysis (FCA). The FOD approach starts with the organization of the data in homogeneous clusters having common properties which allows to deduce the data’s semantic. Then, it models these clusters by an extension of the FCA. This lattice will be used to build a core of ontology that is represented as a set of fuzzy rules. Ontology designer is given this initial ontology expression for further extension by adding concepts and relationships (part-of, related to, etc.). To validate our approach, we used Protégé 4.3, that support the fuzzy concept and generates automatically the script in fuzzy-OWL 2 language.

Pages: 19 to 25

Copyright: Copyright (c) IARIA, 2014

Publication date: April 20, 2014

Published in: conference

ISSN: 2308-4332

ISBN: 978-1-61208-334-6

Location: Chamonix, France

Dates: from April 20, 2014 to April 24, 2014