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A Formal Knowledge Representation System (FKRS) for the Intelligent Knowledge Base of a Cognitive Learning Engine

Copyright © 2013. 15 pages.
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DOI: 10.4018/978-1-4666-2651-5.ch001
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MLA

Tian, Yousheng, Yingxu Wang, Marina L. Gavrilova and Guenther Ruhe. "A Formal Knowledge Representation System (FKRS) for the Intelligent Knowledge Base of a Cognitive Learning Engine." Advances in Abstract Intelligence and Soft Computing. IGI Global, 2013. 1-15. Web. 27 Aug. 2014. doi:10.4018/978-1-4666-2651-5.ch001

APA

Tian, Y., Wang, Y., Gavrilova, M. L., & Ruhe, G. (2013). A Formal Knowledge Representation System (FKRS) for the Intelligent Knowledge Base of a Cognitive Learning Engine. In Y. Wang (Ed.), Advances in Abstract Intelligence and Soft Computing (pp. 1-15). Hershey, PA: Information Science Reference. doi:10.4018/978-1-4666-2651-5.ch001

Chicago

Tian, Yousheng, Yingxu Wang, Marina L. Gavrilova and Guenther Ruhe. "A Formal Knowledge Representation System (FKRS) for the Intelligent Knowledge Base of a Cognitive Learning Engine." In Advances in Abstract Intelligence and Soft Computing, ed. Yingxu Wang, 1-15 (2013), accessed August 27, 2014. doi:10.4018/978-1-4666-2651-5.ch001

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Abstract

It is recognized that the generic form of machine learning is a knowledge acquisition and manipulation process mimicking the brain. Therefore, knowledge representation as a dynamic concept network is centric in the design and implementation of the intelligent knowledge base of a Cognitive Learning Engine (CLE). This paper presents a Formal Knowledge Representation System (FKRS) for autonomous concept formation and manipulation based on concept algebra. The Object-Attribute-Relation (OAR) model for knowledge representation is adopted in the design of FKRS. The conceptual model, architectural model, and behavioral models of the FKRS system is formally designed and specified in Real-Time Process Algebra (RTPA). The FKRS system is implemented in Java as a core component towards the development of the CLE and other knowledge-based systems in cognitive computing and computational intelligence.
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1. Introduction

Knowledge representation is recognized as a central problem in machine learning. Traditional technologies for knowledge representation are relational knowledge bases, natural language processing (NLP) technologies, and ontology (Crystal, 1987; Pullman, 1997; Brewster et al., 2004; Leone et al., 2010; Wang, 2009b; Tian et al., 2009). Knowledge base technologies represent knowledge by lexical and semantic relations (Debenham, 1989). WordNet and ConceptNet are typical lexical databases (Fellbaum, 1998; Liu & Singh, 2004). Various rule-based systems are developed for knowledge representation using logical rules (Bender, 1996) and fuzzy rules (Zadeh, 1965, 2004; Surmann, 2000). NLP technologies are developed for text processing in natural languages (Liddy, 2001; Wilson & Keil, 2001). Although various methods were proposed in NLP, fundamental technologies of them can be classified into two categories such as the symbolic approach (Chomsky, 1957) and the computational linguistic approach (Pullman, 1997). The former treats language as character strings with syntactic relations such as formal grammars (Chomsky, 1957; Burton, 1976; Kaplan & Bresnan, 1982; Wang, 2009a) and text parsing (McDermid, 1991; Wang, 2010b). The latter studies computational processing of natural languages such as the translation theory (Weaver, 1949; Crystal, 1987) and information retrieval techniques (Chang et al., 2006; Zhao & Sui, 2008; Reisinger & Pasca, 2009; Hu et al., 2010). However, the NPL technologies lack detailed analytic power at the concept and attribute levels underpinning semantic analyses at the word-level (Burton, 1976; Wang, 2008b, 2010b). Ontology is the third approach to knowledge representation and modeling, which is a branch of metaphysics dealing with the nature of being, which treats a small-scale knowledge as a set of words and their semantic relations in a certain domain (Gruber, 1993; Cocchiarella, 1996; Brewster et al., 2004; Tiberino et al., 2005; Sanchez, 2010; Hao, 2010; Wang et al., 2011). However, ontology may only represent a set of static knowledge and is highly application specific. Therefore, ontology was not designed to enable machines to automatically generate and manipulate concept networks for knowledge representation as that of human beings.

In recent studies in cognitive informatics (Wang, 2007c) and cognitive computing (Wang, 2009c, 2010a), it is recognized that concepts are the basic unit of human thinking, reasoning, and communications (Pojman, 2003; Wang, 2008b). An internal knowledge representation theory known as the Object-Attribute-Relation (OAR) model is proposed by Wang (2007a), which reveals the logical foundation of concepts and their attributes based on physiological and biological observations (Wilson & Keil, 2001). The OAR model provides a logical view of the long-term memory of the brain, which is a triple (O, A, R), where O is a finite set of objects identified by unique symbolic names; A is a finite set of attributes for characterizing the objects; and R is a set of relations between an object and other objects or their attributes.

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Complete Chapter List

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Table of Contents
Preface
Yingxu Wang
Chapter 1
Yousheng Tian, Yingxu Wang, Marina L. Gavrilova, Guenther Ruhe
It is recognized that the generic form of machine learning is a knowledge acquisition and manipulation process mimicking the brain. Therefore... Sample PDF
A Formal Knowledge Representation System (FKRS) for the Intelligent Knowledge Base of a Cognitive Learning Engine
$37.50
Chapter 2
Yuanyuan Zuo, Bo Zhang
The sparse representation based classification algorithm has been used to solve the problem of human face recognition, but the image database is... Sample PDF
Sparse Based Image Classification With Bag-of-Visual-Words Representations
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Chapter 3
Yuhong Chi, Fuchun Sun, Langfan Jiang, Chunyang Yu, Chunli Chen
To control particles to fly inside the limited search space and deal with the problems of slow search speed and premature convergence of particle... Sample PDF
Quotient Space-Based Boundary Condition for Particle Swarm Optimization Algorithm
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Chapter 4
Ahmed Kharrat, Karim Gasmi, Mohamed Ben Messaoud, Nacéra Benamrane, Mohamed Abid
A new approach for automated diagnosis and classification of Magnetic Resonance (MR) human brain images is proposed. The proposed method uses... Sample PDF
Medical Image Classification Using an Optimal Feature Extraction Algorithm and a Supervised Classifier Technique
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Chapter 5
Ling Zou, Xinguang Wang, Guodong Shi, Zhenghua Ma
Accurate classification of EEG left and right hand motor imagery is an important issue in brain-computer interface. Firstly, discrete wavelet... Sample PDF
EEG Feature Extraction and Pattern Classification Based on Motor Imagery in Brain-Computer Interface
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Chapter 6
Du Zhang, Meiliu Lu
One of the long-term research goals in machine learning is how to build never-ending learners. The state-of-the-practice in the field of machine... Sample PDF
Inconsistency-Induced Learning for Perpetual Learners
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Chapter 7
Tianyong Hao, Feifei Xu, Jingsheng Lei, Liu Wenyin, Qing Li
A strategy of automatic answer retrieval for repeated or similar questions in user-interactive systems by employing semantic question patterns is... Sample PDF
Toward Automatic Answers in User-Interactive Question Answering Systems
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Chapter 8
Yingxu Wang
Human thought, perception, reasoning, and problem solving are highly dependent on causal inferences. This paper presents a set of cognitive models... Sample PDF
On Cognitive Models of Causal Inferences and Causation Networks
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Chapter 9
Du Zhang
Inconsistency is commonplace in the real world in long-term memory and knowledge based systems. Managing inconsistency is considered a hallmark of... Sample PDF
On Localities of Knowledge Inconsistency
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Chapter 10
Ping Chen, Wei Ding, Walter Garcia
Association mining aims to find valid correlations among data attributes, and has been widely applied to many areas of data analysis. This paper... Sample PDF
Adaptive Study Design Through Semantic Association Rule Analysis
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Chapter 11
Shinichiro Sega, Hirotoshi Iwasaki, Hironori Hiraishi, Fumio Mizoguchi
This paper explores applying qualitative reasoning to a driver’s mental state in real driving situations so as to develop a working load for... Sample PDF
Qualitative Reasoning Approach to a Driver’s Cognitive Mental Load
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Chapter 12
Cyprian F. Ngolah, Ed Morden, Yingxu Wang
Monitoring industrial machine health in real-time is not only in high demand, it is also complicated and difficult. Possible reasons for this... Sample PDF
Intelligent Fault Recognition and Diagnosis for Rotating Machines using Neural Networks
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Chapter 13
Yingxu Wang, Vincent Chiew
Functional complexity is one of the most fundamental properties of software because almost all other software attributes and properties such as... Sample PDF
Empirical Studies on the Functional Complexity of Software in Large-Scale Software Systems
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Chapter 14
Yingxu Wang, Cyprian F. Ngolah, Xinming Tan, Yousheng Tian, Phillip C.Y. Sheu
Files are a typical abstract data type for data objects and software modeling, which provides a standard encapsulation and access interface for... Sample PDF
The Formal Design Model of a File Management System (FMS)
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Chapter 15
Yingxu Wang, Cyprian F. Ngolah, Xinming Tan, Phillip C.Y. Sheu
Abstract Data Types (ADTs) are a set of highly generic and rigorously modeled data structures in type theory. Lists as a finite sequence of elements... Sample PDF
The Formal Design Model of Doubly-Linked-Circular Lists (DLC-Lists)
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Chapter 16
Juan L. G. Guirao, Fernando L. Pelayo
This paper provides an overview over the relationship between Petri Nets and Discrete Event Systems as they have been proved as key factors in the... Sample PDF
Petri Nets and Discrete Events Systems
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Chapter 17
Yingxu Wang, Jason Huang, Jingsheng Lei
Arrays are one of the most fundamental and widely applied data structures, which are useful for modeling both logical designs and physical... Sample PDF
The Formal Design Models of a Universal Array (UA) and its Implementation
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Chapter 18
Yingxu Wang, Xinming Tan
Trees are one of the most fundamental and widely used non-linear hierarchical structures of linked nodes. A binary tree (B-Tree) is a typical... Sample PDF
The Formal Design Models of Tree Architectures and Behaviors
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Chapter 19
Yuji Wang, Fuchun Sun, Huaping Liu
The four-channel architecture in teleoperation with force feedback has been studied in various existing literature. However, most of them focused on... Sample PDF
Four-Channel Control Architectures for Bilateral and Multilateral Teleoperation
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Chapter 20
Rosanne Vetro, Dan A. Simovici, Wei Ding
This paper introduces entropy quad-trees, which are structures derived from quad-trees by allowing nodes to split only when those correspond to... Sample PDF
Entropy Quad-Trees for High Complexity Regions Detection
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Chapter 21
Yusuke Manabe, Kenji Sugawara
Realization of human-computer symbiosis is an important idea in the context of ubiquitous computing. Symbiotic Computing is a concept that bridges... Sample PDF
Sitting Posture Recognition and Location Estimation for Human-Aware Environment
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Chapter 22
Yunlong Wang, Kueiming Lo
Generic cabling is a key component for multiplex cable wiring. It is one of the basic foundations of intelligent buildings. Using operation flow in... Sample PDF
Generic Cabling of Intelligent Buildings Based on Ant Colony Algorithm
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Chapter 23
Ricardo Rodriguez, Ivo Bukovsky, Noriyasu Homma
The paper discusses the quadratic neural unit (QNU) and highlights its attractiveness for industrial applications such as for plant modeling... Sample PDF
Potentials of Quadratic Neural Unit for Applications
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Chapter 24
Du Zhang
The fundamental objective in value-based software engineering is to integrate consistent stakeholder value propositions into the full extent of... Sample PDF
A Value-Based Framework for Software Evolutionary Testing
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Chapter 25
A. Meera, Lalitha Rangarajan
Understanding how the regulation of gene networks is orchestrated is an important challenge for characterizing complex biological processes. The DNA... Sample PDF
Comparison of Promoter Sequences Based on Inter Motif Distance
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