A multivalued knowledge-base model

TL;DR

This paper proposes a multivalued knowledge-base model within the DATALOG framework to handle uncertain information, extending fuzzy logic concepts to include intuitionistic, interval-valued, and bipolar fuzzy logic, with an evaluation strategy.

Contribution

It introduces a novel multivalued knowledge-base model that integrates various fuzzy logic extensions into DATALOG for managing uncertainty.

Findings

Defines a multivalued knowledge-base as a quadruple including background knowledge and deduction mechanisms

Extends fuzzy Datalog to intuitionistic, interval-valued, and bipolar fuzzy logic

Provides an evaluation strategy for the proposed model

Abstract

The basic aim of our study is to give a possible model for handling uncertain information. This model is worked out in the framework of DATALOG. At first the concept of fuzzy Datalog will be summarized, then its extensions for intuitionistic- and interval-valued fuzzy logic is given and the concept of bipolar fuzzy Datalog is introduced. Based on these ideas the concept of multivalued knowledge-base will be defined as a quadruple of any background knowledge; a deduction mechanism; a connecting algorithm, and a function set of the program, which help us to determine the uncertainty levels of the results. At last a possible evaluation strategy is given.