Neutrosophic Relational Data Model

TL;DR

This paper introduces a neutrosophic relational data model that effectively manages incomplete and inconsistent information using specialized operators, extending traditional fuzzy and intuitionistic fuzzy relations.

Contribution

It proposes a novel neutrosophic data model with operators for handling inconsistency, enabling advanced manipulation of uncertain and conflicting data in databases.

Findings

Introduces 'split' and 'combine' operators for inconsistency management.

Defines algebraic operators for neutrosophic relations.

Supports manipulation of incomplete and inconsistent information.

Abstract

In this paper, we present a generalization of the relational data model based on interval neutrosophic set. Our data model is capable of manipulating incomplete as well as inconsistent information. Fuzzy relation or intuitionistic fuzzy relation can only handle incomplete information. Associated with each relation are two membership functions one is called truth-membership function T which keeps track of the extent to which we believe the tuple is in the relation, another is called falsity-membership function F which keeps track of the extent to which we believe that it is not in the relation. A neutrosophic relation is inconsistent if there exists one tuple a such that T(a) + F(a) > 1 . In order to handle inconsistent situation, we propose an operator called "split" to transform inconsistent neutrosophic relations into pseudo-consistent neutrosophic relations and do the set-theoretic and relation-theoretic operations on them and finally use another operator called "combine" to transform the result back to neutrosophic relation. For this data model, we define algebraic operators that are generalizations of the usual operators such as intersection, union, selection, join on fuzzy relations. Our data model can underlie any database and knowledge-base management system that deals with incomplete and inconsistent information.