A New LCS-Neutrosophic Similarity Measure for Text Information Retrieval

A New LCS-Neutrosophic Similarity Measure for Text Information Retrieval

Misturah Adunni Alaran (Moshood Abiola Polytechnic, Abeokuta, Nigeria), AbdulAkeem Adesina Agboola (Federal University of Agriculture, Nigeria), Adio Taofiki Akinwale (Federal University of Agriculture, Nigeria) and Olusegun Folorunso (Federal University of Agriculture, Nigeria)
Copyright: © 2020 |Pages: 23
DOI: 10.4018/978-1-7998-2555-5.ch012

Abstract

The reality of human existence and their interactions with various things that surround them reveal that the world is imprecise, incomplete, vague, and even sometimes indeterminate. Neutrosophic logic is the only theory that attempts to unify all previous logics in the same global theoretical framework. Extracting data from a similar environment is becoming a problem as the volume of data keeps growing day-in and day-out. This chapter proposes a new neutrosophic string similarity measure based on the longest common subsequence (LCS) to address uncertainty in string information search. This new method has been compared with four other existing classical string similarity measure using wordlist as data set. The analyses show the performance of proposed neutrosophic similarity measure to be better than the existing in information retrieval task as the evaluation is based on precision, recall, highest false match, lowest true match, and separation.
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Introduction

Proper classification of data which forms the basis of any form of research or reason for solving any research problem has been identified. There are three classes, these are; classical set, fuzzy set and neutrosophic set, each has been identified to be different from the other as the inadequacy of one (in order) gave rise to the emergence of the other. Explanation of how one led to the other, has been given. Also, solving problems relating to human endeavor, the introduction of the neutrosophic set has helped greatly in modeling indeterminacy of which the classical nor the fuzzy sets could handle (Agboola,2016). The process of information searching is assumed to be a means of attempting to resolve some uncertainty in knowledge (Kuhlthau, 1993; 2003). This uncertainty principle can be associated with the ASK (Anomalous State of Knowledge) model. This opined that as the user realizes the inadequacy in his or her state of knowledge, he resorts to information searching to clear the impreciseness. At the initial stage of a problem, it may be impossible for the user to specify precisely what is lacking in his or her knowledge state. In a broad sense, uncertainty describes a situation where a user’s knowledge is limited. This uncertainty may be how to express a need, what that need means, or the changing of previously held beliefs (Belkin, Oddy & Brooks, 1982).

An understanding of neutrosophic axioms has been proposed where the difference between the two types of communication has been presented. These are Neutrosophic communication and informational communication. Neutrosophic communication was expressed as the type of communication in which the message consists of incomplete, vague and imprecise elements while informational communication describes a communication whose aim is to share an informational message. Smarandache and Vlăduțescu (2013) affirmed neutrosophic communication as the rule while informational communication as the exception in the ideal sense of communication. In considering a world full of indeterminacy, traditional crisp set with its boundaries of truth and false lacks the ability to reflect the reality. As a result of these claims, neutrosophy is used in recent research as a new representation of the real world as propounded by Florentin Smarandache (Smaradanche,1995). Also, in determining the association between any two entities, distance and similarity measures are very useful techniques (Bhattacharyya, Koli & Majumdar, 2018). To deal with uncertainties in searching or comparing some entities for resemblance, neutrosophic similarity methods will be an appropriate tool to present a realistic situation. This chapter presents the Longest Common Subsequence (LCS) Neutrosophic string similarity measure and its performance is compared with existing classical similarity measures. These results are evaluated to determine the efficiency of the proposed string neutrosophic similarity measure.

Key Terms in this Chapter

Query: A question for search.

Neutrosophy: A new study concerning uncertainty phenomenon.

Precision: A measure of ascertaining a fact.

Indeterminate: Neither here nor there.

String: A combination of letters, numbers, and/or some special character.

Retrieval: The act of bringing out something from a volume of content.

Similarity: The sense of creating a resemblance or association between two or more entities.

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