Using Rough Set Theory for Reasoning on Vague Ontologies

Abstract

Web ontologies can contain vague concepts, which means the knowledge about them is imprecise and then query answering will not possible due to the open world assumption. A concept description can be very exact (crisp concept) or exact (fuzzy concept) if its knowledge is complete, otherwise it is inexact (vague concept) if its knowledge is incomplete. In this paper, we propose a method based on the rough set theory for reasoning on vague ontologies. With this method, the detection of vague concepts will insert into the original ontology new rough vague concepts where their description is defined on approximation spaces to be used by extended Tableau algorithm for automatic reasoning. A prototype of Tableau's extended algorithm is developed and tested on examples where encouraging results are given by this method to demonstrate that unlike other methods, it is possible to answer queries even in the presence of incomplete information.

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“…Many scientists are currently researching problems related to vagueness [1]. Discovering effective knowledge from ambiguous data has become a major area of research [2], leading to the development of several techniques for identifying uncertain information, such as fuzzy set (FS) theory [3], quotient space theory [4], and rough set theory (RST) [5].…”

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“…In 1986, Atanassov [9] proposed the idea of IFS as a means of representing ambiguous and complex information using membership grades (MGs) and non- n conclusion, we highlight that the developed approach is highly suitable for olicies, and we anticipate its extension to other fuzzy information domains. tic fuzzy sets; hesitant fuzzy sets; three-way decision; Aczel-Alsina aggregaof vagueness-related issues has become a central focus among scholf extracting meaningful insights from imperfect data has emerged as arch [2]. Consequently, various techniques, including fuzzy set (FS) pace theory [4], and rough set theory (RST) [5], have been developed ambiguous information.…”

“…In 1986, Atanassov [9] proposed the idea of IFS as a means of representing ambiguous and complex information using membership grades (MGs) and non- The exploration of vagueness-related issues has become a central focus among scholars [1]. The pursuit of extracting meaningful insights from imperfect data has emerged as a critical area of research [2]. Consequently, various techniques, including fuzzy set (FS) theory [3], quotient space theory [4], and rough set theory (RST) [5], have been developed to detect and tackle ambiguous information.…”

“…. The pursuit of extracting meaningful insights from imperfect data has emerged as itical area of research [2]. Consequently, various techniques, including fuzzy set (FS) ry [3], quotient space theory [4], and rough set theory (RST) [5], have been developed etect and tackle ambiguous information.…”

“…The exploration of vagueness-related issues has become a central focus among scholars [1]. The pursuit of extracting meaningful insights from imperfect data has emerged as a critical area of research [2]. Consequently, various techniques, including fuzzy set (FS) theory [3], quotient space theory [4], and rough set theory (RST) [5], have been developed Air University, E-9, Islamabad 44000, Pakistan; tanzeela.shaheen@mail.au.edu.pk artment, Riphah International University, Islamabad 46000, Pakistan; du.pk (H.G.T.…”