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

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

Yousheng Tian (University of Calgary, Canada), Yingxu Wang (University of Calgary, Canada), Marina L. Gavrilova (University of Calgary, Canada) and Guenther Ruhe (University of Calgary, Canada)
Copyright: © 2013 |Pages: 15
DOI: 10.4018/978-1-4666-2651-5.ch001
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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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Table of Contents
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
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
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
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
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
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
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
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
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
Chapter 10
Ping Chen, Wei Ding, Walter Garcia
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Adaptive Study Design Through Semantic Association Rule Analysis
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
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
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
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)
Chapter 15
Yingxu Wang, Cyprian F. Ngolah, Xinming Tan, Phillip C.Y. Sheu
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The Formal Design Model of Doubly-Linked-Circular Lists (DLC-Lists)
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
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
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
Chapter 19
Yuji Wang, Fuchun Sun, Huaping Liu
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Chapter 20
Rosanne Vetro, Dan A. Simovici, Wei Ding
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Entropy Quad-Trees for High Complexity Regions Detection
Chapter 21
Yusuke Manabe, Kenji Sugawara
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Chapter 22
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Chapter 23
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Chapter 24
Du Zhang
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Chapter 25
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