SATISFIABILITY REASONING OVER VAGUE ONTOLOGIES USING FUZZY SOFT SET THEORY

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SATISFIABILITY REASONING OVER VAGUE ONTOLOGIES USING FUZZY SOFT SET THEORY

 

Chapter One

Introduction

1.1.  Background to the Study

The use of information from heterogeneous sources is an intelligent task that requires human being who has a background knowledge of the information. However, due to existence of several information sources, human processing speed cannot be relied upon for speedy information processing. In contrast, computers can easily deal with such voluminous information as long as their processing do not require human intelligence. Therefore, for information to be processed efficiently, its processing must be automated. Almost all information can be represented in natural language, this richness of natural language, however, makes it very difficult to process computationally. The traditional computational processing of information involved a pattern matching process, a literal character by character comparison of the words in natural languages representing information. This simplistic computational processing approach is known as syntactic information processing. On the contrary, semantics information representation provides a universal understanding of information (both by human and machine) and leads to automated information processing. This can be achieved by attaching meaning to letters, words, phrases, signs, and symbols. Semantic information processing is seen as a means of resolving the problem of ambiguities in syntactic information processing (Richardson, 1994).

While talking about automated processing for natural language, Miller (1995) stated that, because meaningful sentences are composed of meaningful words, any system that hopes to process natural languages as people do must have information about words and their meanings. This information is traditionally provided through dictionaries, and machine-readable dictionaries are now widely available. But dictionary entries evolved for the convenience of human readers, not for machines.

According to Kana and Akinkunmi (2014), for the semantic processing of information to be possible, systems must be able to understand the meaning of data they are processing and then, perform the processing semantically. To achieve that, three key issues must be resolved:

  1. Information should be represented in such a way that, its semantics is contained within its representation and should be unambiguous.
  2. There should be a possibility of deducing the semantic of the data represented by machines possibly with some inference capability.
  3. There should be a possibility of two or more system processing related information to interoperate.

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SATISFIABILITY REASONING OVER VAGUE ONTOLOGIES USING FUZZY SOFT SET THEORY