ON CONCEPT ALGEBRA FOR COMPUTING WITH WORDS (CWW)

2010 ◽  
Vol 04 (03) ◽  
pp. 331-356 ◽  
Author(s):  
YINGXU WANG
Keyword(s):  
Computing Methodology ◽  
System Modeling ◽  
Formal Theory ◽  
Mathematical Structure ◽  
Computing With Words ◽  
Linguistic Variables ◽  
Cognitive Computing ◽  
Human Beings ◽  
Intelligent Computing ◽  
Concrete Entity

Computing with words (CWW) is an intelligent computing methodology for processing words, linguistic variables, and their semantics, which mimics the natural-language-based reasoning mechanisms of human beings in soft computing, semantic computing, and cognitive computing. The central objects in CWW techniques are words and linguistic variables, which may be formally modeled by abstract concepts that are a basic cognitive unit to identify and model a concrete entity in the real world and an abstract object in the perceived world. Therefore, concepts are the most fundamental linguistic entities that carries certain meanings in expression, thinking, reasoning, and system modeling, which may be formally modeled as an abstract and dynamic mathematical structure in denotational mathematics. This paper presents a formal theory for concept and knowledge manipulations in CWW known as concept algebra. The mathematical models of abstract and concrete concepts are developed based on the object-attribute-relation (OAR) theory. The formal methodology for manipulating knowledge as a concept network is described. Case studies demonstrate that concept algebra provides a generic and formal knowledge manipulation means, which is capable of dealing with complex knowledge and their algebraic operations in CWW.

Semantic Manipulations and Formal Ontology for Machine Learning based on Concept Algebra

2012 ◽  
pp. 194-220
Author(s):  
Yingxu Wang ◽  
Yousheng Tian ◽  
Kendal Hu
Keyword(s):  
Machine Learning ◽  
Operational Semantics ◽  
Mathematical Structure ◽  
Expressive Power ◽  
Formal Ontology ◽  
Cognitive Computing ◽  
Human Beings ◽  
Knowledge Modeling ◽  
Wide Range ◽  
Cognitive Robots

Towards the formalization of ontological methodologies for dynamic machine learning and semantic analyses, a new form of denotational mathematics known as concept algebra is introduced. Concept Algebra (CA) is a denotational mathematical structure for formal knowledge representation and manipulation in machine learning and cognitive computing. CA provides a rigorous knowledge modeling and processing tool, which extends the informal, static, and application-specific ontological technologies to a formal, dynamic, and general mathematical means. An operational semantics for the calculus of CA is formally elaborated using a set of computational processes in real-time process algebra (RTPA). A case study is presented on how machines, cognitive robots, and software agents may mimic the key ability of human beings to autonomously manipulate knowledge in generic learning using CA. This work demonstrates the expressive power and a wide range of applications of CA for both humans and machines in cognitive computing, semantic computing, machine learning, and computational intelligence.

Semantic Manipulations and Formal Ontology for Machine Learning based on Concept Algebra

2011 ◽  
Vol 5 (3) ◽  
pp. 1-29 ◽  
Author(s):  
Yingxu Wang ◽  
Yousheng Tian ◽  
Kendal Hu
Keyword(s):  
Machine Learning ◽  
Operational Semantics ◽  
Mathematical Structure ◽  
Expressive Power ◽  
Formal Ontology ◽  
Cognitive Computing ◽  
Human Beings ◽  
Knowledge Modeling ◽  
Wide Range ◽  
Cognitive Robots

Towards the formalization of ontological methodologies for dynamic machine learning and semantic analyses, a new form of denotational mathematics known as concept algebra is introduced. Concept Algebra (CA) is a denotational mathematical structure for formal knowledge representation and manipulation in machine learning and cognitive computing. CA provides a rigorous knowledge modeling and processing tool, which extends the informal, static, and application-specific ontological technologies to a formal, dynamic, and general mathematical means. An operational semantics for the calculus of CA is formally elaborated using a set of computational processes in real-time process algebra (RTPA). A case study is presented on how machines, cognitive robots, and software agents may mimic the key ability of human beings to autonomously manipulate knowledge in generic learning using CA. This work demonstrates the expressive power and a wide range of applications of CA for both humans and machines in cognitive computing, semantic computing, machine learning, and computational intelligence.

On Concept Algebra

2011 ◽  
pp. 126-148
Author(s):  
Yingxu Wang
Keyword(s):  
System Modeling ◽  
Formal Theory ◽  
Mathematical Structure ◽  
Abstract Concept ◽  
Abstract Concepts ◽  
Formal Knowledge ◽  
Adaptive Capability ◽  
Algebraic Operations ◽  
Complex Knowledge ◽  
Object Attribute

Concepts are the most fundamental unit of cognition that carries certain meanings in expression, thinking, reasoning, and system modeling. In denotational mathematics, a concept is formally modeled as an abstract and dynamic mathematical structure that encapsulates attributes, objects, and relations. The most important property of an abstract concept is its adaptive capability to autonomously interrelate itself to other concepts. This article presents a formal theory for abstract concepts and knowledge manipulation known as “concept algebra.” The mathematical models of concepts and knowledge are developed based on the object-attribute-relation (OAR) theory. The formal methodology for manipulating knowledge as a concept network is described. Case studies demonstrate that concept algebra provides a generic and formal knowledge manipulation means, which is capable to deal with complex knowledge and software structures as well as their algebraic operations.

On Concept Algebra

2009 ◽  
pp. 3055-3075
Author(s):  
Yingxu Wang
Keyword(s):  
System Modeling ◽  
Formal Theory ◽  
Mathematical Structure ◽  
Abstract Concept ◽  
Abstract Concepts ◽  
Formal Knowledge ◽  
Adaptive Capability ◽  
Algebraic Operations ◽  
Complex Knowledge ◽  
Object Attribute

Concepts are the most fundamental unit of cognition that carries certain meanings in expression, thinking, reasoning, and system modeling. In denotational mathematics, a concept is formally modeled as an abstract and dynamic mathematical structure that encapsulates attributes, objects, and relations. The most important property of an abstract concept is its adaptive capability to autonomously interrelate itself to other concepts. This article presents a formal theory for abstract concepts and knowledge manipulation known as “concept algebra.” The mathematical models of concepts and knowledge are developed based on the object-attribute-relation (OAR) theory. The formal methodology for manipulating knowledge as a concept network is described. Case studies demonstrate that concept algebra provides a generic and formal knowledge manipulation means, which is capable to deal with complex knowledge and software structures as well as their algebraic operations.

Perspectives on Cognitive Informatics and Cognitive Computing

2010 ◽  
Vol 4 (1) ◽  
pp. 1-29 ◽  
Author(s):  
Yingxu Wang ◽  
George Baciu ◽  
Yiyu Yao ◽  
Witold Kinsner ◽  
Keith Chan ◽  
...  
Keyword(s):  
Computational Linguistics ◽  
Computational Intelligence ◽  
Cognitive Computing ◽  
Cognitive Informatics ◽  
Intelligent Computing ◽  
Internal Information ◽  
Processing Mechanisms ◽  
The Brain ◽  
Science Information ◽  
Intelligence Science

Cognitive informatics is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. Cognitive computing is an emerging paradigm of intelligent computing methodologies and systems based on cognitive informatics that implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a set of collective perspectives on cognitive informatics and cognitive computing, as well as their applications in abstract intelligence, computational intelligence, computational linguistics, knowledge representation, symbiotic computing, granular computing, semantic computing, machine learning, and social computing.

Justice, Respect, and Recognition in Mental Health Services

2016 ◽  
Author(s):  
Paul Brodwin
Keyword(s):  
Social Relations ◽  
Health Care Services ◽  
Formal Theory ◽  
Theoretical Models ◽  
Human Beings ◽  
Cultural Discourses ◽  
Care Services ◽  
Psychiatric Survivor ◽  
Interdisciplinary Study ◽  
Harmful Effects

This chapter raises a key question for the interdisciplinary study of health and justice: is dialogue possible between theoretical models and first-person testimony about the harms caused by injustice? To consider this question, the chapter examines the claim that disrespect—the systematic devaluation of others in a way that excludes them from reciprocal social relations—is a form of injustice. The philosopher Stephen Darwall and social theorist Axel Honneth conceptually elucidate the links between justice, respect, and recognition. Their normative arguments offer a high-order conceptual framework for recognizing people’s equal worth as human beings (and the harmful effects of denying such recognition). This chapter compares their abstract frameworks with a landmark autobiography by a founder of the psychiatric survivor movement. The search for commensurability between these texts exposes the precise difference between experience-far and experience-near genres of ethical expression. This chapter adopts a similar approach as DeBruin et al. (this volume) in examining popular cultural discourses in light of formal theory. Both chapters take seriously the lay narratives and forms of ethical argumentation that circulate outside the academy. Both envision a plural ethics of justice and health that acknowledges how ordinary people interpret and respond to institutionalized oppression in health-care services.

Applying Fuzzy Logic and Fuzzy Methods to Marketing

2012 ◽  
pp. 1056-1068
Author(s):  
Laurent Donzé ◽  
Andreas Meier
Keyword(s):  
Fuzzy Logic ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Future Research ◽  
Computing With Words ◽  
Linguistic Variables ◽  
Research Directions ◽  
Fuzzy Methods ◽  
Future Research Directions

Marketing deals with identifying and meeting the needs of customers. It is therefore both an art and a science. To bridge the gap between art and science, soft computing, or computing with words, could be an option. This chapter introduces fundamental concepts such as fuzzy sets, fuzzy logic, and computing with linguistic variables and terms. This set of fuzzy methods can be applied in marketing and customer relationship management. In the conclusion, future research directions are given for applying fuzzy logic to marketing and customer relationship management.

Perspectives on Cognitive Informatics and Cognitive Computing

2012 ◽  
pp. 1-24
Author(s):  
Yingxu Wang ◽  
George Baciu ◽  
Yiyu Yao ◽  
Witold Kinsner ◽  
Keith Chan ◽  
...  
Keyword(s):  
Computational Linguistics ◽  
Computational Intelligence ◽  
Social Computing ◽  
Cognitive Computing ◽  
Cognitive Informatics ◽  
Intelligent Computing ◽  
Internal Information ◽  
Processing Mechanisms ◽  
The Brain ◽  
Science Information

Cognitive informatics is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. Cognitive computing is an emerging paradigm of intelligent computing methodologies and systems based on cognitive informatics that implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a set of collective perspectives on cognitive informatics and cognitive computing, as well as their applications in abstract intelligence, computational intelligence, computational linguistics, knowledge representation, symbiotic computing, granular computing, semantic computing, machine learning, and social computing.

On Cognitive Computing

2012 ◽  
pp. 83-97
Author(s):  
Yingxu Wang
Keyword(s):  
Computational Intelligence ◽  
Autonomic Computing ◽  
Theoretical Framework ◽  
Autonomous Agent ◽  
Cognitive Computing ◽  
Cognitive Informatics ◽  
Intelligent Computing ◽  
Agent Systems ◽  
The Brain ◽  
Theoretical Foundations

Inspired by the latest development in cognitive informatics and contemporary denotational mathematics, cognitive computing is an emerging paradigm of intelligent computing methodologies and systems, which implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a survey on the theoretical framework and architectural techniques of cognitive computing beyond conventional imperative and autonomic computing technologies. Theoretical foundations of cognitive computing are elaborated from the aspects of cognitive informatics, neural informatics, and denotational mathematics. Conceptual models of cognitive computing are explored on the basis of the latest advances in abstract intelligence and computational intelligence. Applications of cognitive computing are described from the aspects of autonomous agent systems and cognitive search engines, which demonstrate how machine and computational intelligence may be generated and implemented by cognitive computing theories and technologies toward autonomous knowledge processing.

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