Commonsense knowledge representation and reasoning with fuzzy neural networks

This paper highlights the theory of common-sense knowledge in terms of representation and reasoning. A connectionist model is proposed for common-sense knowledge representation and reasoning. A generic fuzzy neuron is employed as a basic element for the connectionist model. The representation and reasoning ability of the model is described through examples

Commonsense knowledge-based face detection

A connectionist model is presented for commonsense knowledge representation and reasoning. The representation and reasoning ability of the model is described through examples. The commonsense knowledge base is employed to develop a human face detection system. The system consists of three stages: preprocessing, face-components extraction, and final decision-making. A neural network-based algorithm is utilised to extract face components. Five networks are trained to detect mouth, nose, eyes, and full face. The detected face components and their corresponding possibility degrees allow the knowledge base to locate faces in the image and generate a membership degree for the detected faces within the face class. The experimental results obtained using this method are presented

Linguistic quantifiers modeled by Sugeno integrals

Since quantifiers have the ability of summarizing the properties of a class of objects without enumerating them, linguistic quantification is a very important topic in the field of high level knowledge representation and reasoning. This paper introduces a new framework for modeling quantifiers in natural languages in which each linguistic quantifier is represented by a family of fuzzy measures, and the truth value of a quantified proposition is evaluated by using Sugeno's integral. This framework allows us to have some elegant logical properties of linguistic quantifiers. We compare carefully our new model of quantification and other approaches to linguistic quantifiers. A set of criteria for linguistic quantification was proposed in the previous literature. The relationship between these criteria and the results obtained in the present paper is clarified. Some simple applications of the Sugeno's integral semantics of quantifiers are presented. © 2006 Elsevier B.V. All rights reserved

Integrity Control in Relational Database Systems - An Overview

This paper gives an overview of research regarding integrity control or integrity constraint handling in relational database management systems. The topic of constraint handling is discussed from two points of view. First, constraint handling is discussed by identifying a number of important research issues, and by treating each issue in detail. Second, a number of projects is described that have resulted in the realization of database management systems supporting integrity constraints; the various projects are compared with respect to a number of system characteristics. Together, both approaches give a broad overview of the state of the art in the field at this moment

Paradigm Shift from Vague Legal Contracts to Blockchain-Based Smart Contracts

In this dissertation, we address the problem of vagueness in traditional legal contracts by presenting novel methodologies that aid in the paradigm shift from traditional legal contracts to smart contracts. We discuss key enabling technologies that assist in converting the traditional natural language legal contract, which is full of vague words, phrases, and sentences to the blockchain-based precise smart contract, including metrics evaluation during our conversion experiment. To address the challenge of this contract-transformation process, we propose four novel proof-of-concept approaches that take vagueness and different possible interpretations into significant consideration, where we experiment with popular vendors' existing vague legal contracts. We show through experiments that our proposed methodologies are able to study the degree of vagueness in every interpretation and demonstrate which vendor's translated-smart contract can be more accurate, optimized, and have a lesser degree of vagueness. We also incorporated the method of fuzzy logic inside the blockchain-based smart contract, to successfully model the semantics of linguistic expressions. Our experiments and results show that the smart contract with the higher degrees of truth can be very complex technically but more accurate at the same time. By using fuzzy logic inside a smart contract, it becomes easier to solve the problem of contractual ambiguities as well as expedite the process of claiming compensation when implemented in a blockchain-based smart contract