Development of an Ontology for an Industrial Domain

Development of an Ontology for an Industrial Domain

Christine W. Chan (University of Regina, Canada)
Copyright: © 2009 |Pages: 15
DOI: 10.4018/978-1-60566-170-4.ch019
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This chapter presents a method for ontology construction and its application in developing ontology in the domain of natural gas pipeline operations. Both the method as well as the application ontology developed, contribute to the infrastructure of Semantic Web that provides semantic foundation for supporting information processing by autonomous software agents. This chapter presents the processes of knowledge acquisition and ontology construction for developing a knowledge-based decision support system for monitoring and control of natural gas pipeline operations. Knowledge on the problem domain was acquired and analyzed using the Inferential Modeling Technique, then the analyzed knowledge was organized into an application ontology and represented in the Knowledge Modeling System. Since ontology is an explicit specification of a conceptualization that provides a comprehensive foundation specification of knowledge in a domain, it provides semantic clarifications for autonomous software agents that process information on the Internet.
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The vast amount of information on the World Wide Web has made it increasingly difficult to access and retrieve the required information or data. In response to the problem, the World Wide Web Consortium (W3C) formally proposed the Semantic Web to be the next evolutionary step for the Web in 2001. The Semantic Web aims to attach semantic information to Web resources and would provide the semantic structure or scaffolding that would enable autonomous software agents to traverse the Web in search of or process information on behalf of users or other systems.

Within this context, ontologies are important from at least two perspectives. First, they provide a key component of the Semantic Web because an ontology can function as a repository of vocabulary of unambiguous domain-related concepts and their meanings anchored in consensus domain knowledge (Jasper et al. 1999). This semantic structure would enable autonomous agents to access and process information on the Web. An example is the use of ontologies to support negotiation in e-commerce (Tamma et al. 2005). Second, ontologies provide sharable knowledge models on particular problem domains for construction of knowledge-based systems in a distributed and open environment such as the Internet. Therefore, from the knowledge engineering perspective, an ontology constitutes a crucial building block for developing knowledge-based systems.

Knowledge engineering is the process of eliciting expertise, organizing it into a computational structure, and representing it in a knowledge-based system. The process of knowledge engineering can be viewed from the cognitive informatics perspective (Wang et al, 2002/06; Patel et al., 2003; Chan et al., 2004; Kinsner et al., 2005; Yao et al., 2006; Wang and Kinsner, 2006; Wang, 2002/03/06/07), with a focus on the problem solving expertise in cognition (Chan 2002). The effort spent in engineering knowledge is often substantial due to the tacit nature of expertise; and the process of acquiring knowledge for building the knowledge base is widely recognized as a major bottleneck in the development process. If several knowledge-based systems on the same problem domain are to be constructed, the effort required to build the knowledge bases for the different systems is often duplicated. A possible solution for the problem is to share any knowledge on a given problem domain that has been acquired among systems. Four different approaches for sharing knowledge have been adopted within the Knowledge Sharing Effort sponsored by Air Force Office of Scientific Research, Defense Advanced Research Projects Agency, the Corporation for National Research Initiatives, and the National Science Foundation (Neches et al. 1998). Similar to their objectives, the work presented here aims to construct ontologies which can overcome the barriers to sharing that arise due to lack of consensus across knowledge bases on vocabulary and semantic interpretations in domain models. A critical step in the process of developing an ontology is performing a detailed analysis of the domain. In this chapter, we present a method for knowledge acquisition and ontology construction to support the development of a knowledge-based system, and we demonstrate application of the method to the domain of natural gas pipeline operations. The proposed method involves first eliciting and organizing knowledge using the Inferential Modelling Technique (IMT), and then—based on the initial classification of knowledge elements in the problem domain— constructing an application ontology using an automated knowledge modeling tool called the Knowledge Modeling System. The knowledge represented in the application ontology provides the basis for implementing the advisory system.

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Editorial Advisory Board
Table of Contents
Yingxu Wang
Chapter 1
Yingxu Wang
Cognitive Informatics (CI) is a transdisciplinary enquiry of the internal information processing mechanisms and processes of the brain and natural... Sample PDF
The Theoretical Framework of Cognitive Informatics
Chapter 2
Withold Kinsner
This chapter provides a review of Shannon and other entropy measures in evaluating the quality of materials used in perception, cognition, and... Sample PDF
Is Entropy Suitable to Characterize Data and Signals for Cognitive Informatics?
Chapter 3
Ismael Rodríguez, Manuel Núñez, Fernando Rubio
Finite State Machines (FSM) are formalisms that have been used for decades to describe the behavior of systems. They can also provide an intelligent... Sample PDF
Cognitive Processes by using Finite State Machines
Chapter 4
Yingxu Wang
An interactive motivation-attitude theory is developed based on the Layered Reference Model of the Brain (LRMB) and the Object-Attribute-Relation... Sample PDF
On the Cognitive Processes of Human Perception with Emotions, Motivations, and Attitudes
Chapter 5
Qingyong Li, Zhiping Shi, Zhongzhi Shi
Sparse coding theory demonstrates that the neurons in the primary visual cortex form a sparse representation of natural scenes in the viewpoint of... Sample PDF
A Selective Sparse Coding Model with Embedded Attention Mechanism
Chapter 6
Yingxu Wang
Theoretical research is predominately an inductive process, while applied research is mainly a deductive process. Both inference processes are based... Sample PDF
The Cognitive Processes of Formal Inferences
Chapter 7
Douglas Griffith, Frank L. Greitzer
The purpose of this article is to re-address the vision of human-computer symbiosis as originally expressed by J.C.R. Licklider nearly a... Sample PDF
Neo-Symbiosis: The Next Stage in the Evolution of Human Information Interaction
Chapter 8
Ray E. Jennings
Although linguistics may treat languages as a syntactic and/or semantic entity that regulates both language production and comprehension, this... Sample PDF
Language, Logic, and the Brain
Chapter 9
Yingxu Wang, Guenther Ruhe
Decision making is one of the basic cognitive processes of human behaviors by which a preferred option or a course of actions is chosen from among a... Sample PDF
The Cognitive Process of Decision Making
Chapter 10
Tiansi Dong
This chapter proposes a commonsense understanding of distance and orientation knowledge between extended objects, and presents a formal... Sample PDF
A Commonsense Approach to Representing Spatial Knowledge Between Extended Objects
Chapter 11
Natalia López, Manuel Núñez, Fernando L. Pelayo
In this chapter we present the formal language, stochastic process algebra (STOPA), to specify cognitive systems. In addition to the usual... Sample PDF
A Formal Specification of the Memorization Process
Chapter 12
Yingxu Wang
Autonomic computing (AC) is an intelligent computing approach that autonomously carries out robotic and interactive applications based on goal- and... Sample PDF
Theoretical Foundations of Autonomic Computing
Chapter 13
Witold Kinsner
Numerous attempts are being made to develop machines that could act not only autonomously, but also in an increasingly intelligent and cognitive... Sample PDF
Towards Cognitive Machines: Multiscale Measures and Analysis
Chapter 14
Amar Ramdane-Cherif
Cognitive approach through the neural network (NN) paradigm is a critical discipline that will help bring about autonomic computing (AC). NN-related... Sample PDF
Towards Autonomic Computing: Adaptive Neural Network for Trajectory Planning
Chapter 15
Lee Flax
We give an approach to cognitive modelling, which allows for richer expression than the one based simply on the firing of sets of neurons. The... Sample PDF
Cognitive Modelling Applied to Aspects of Schizophrenia and Autonomic Computing
Chapter 16
Yan Zhao, Yiyu Yao
Classification is one of the main tasks in machine learning, data mining, and pattern recognition. Compared with the extensively studied automation... Sample PDF
Interactive Classification Using a Granule Network
Chapter 17
Mehdi Najjar, André Mayers
Encouraging results of last years in the field of knowledge representation within virtual learning environments confirms that artificial... Sample PDF
A Cognitive Computational Knowledge Representation Theory
Chapter 18
Du Zhang
A crucial component of an intelligent system is its knowledge base that contains knowledge about a problem domain. Knowledge base development... Sample PDF
A Fixpoint Semantics for Rule-Base Anomalies
Chapter 19
Christine W. Chan
This chapter presents a method for ontology construction and its application in developing ontology in the domain of natural gas pipeline... Sample PDF
Development of an Ontology for an Industrial Domain
Chapter 20
Václav Rajlich, Shaochun Xu
This article explores the non-monotonic nature of the programmer learning that takes place during incremental program development. It uses a... Sample PDF
Constructivist Learning During Software Development
Chapter 21
Witold Kinsner
Many scientific chapters treat the diversity of fractal dimensions as mere variations on either the same theme or a single definition. There is a... Sample PDF
A Unified Approach to Fractal Dimensions
Chapter 22
Du Zhang, Witold Kinsner, Jeffrey Tsai, Yingxu Wang, Philip Sheu, Taehyung Wang
The 2005 IEEE International Conference on Cognitive Informatics (ICCI’05) was held during August 8th to 10th 2005 on the campus of University of... Sample PDF
Cognitive Informatics: Four Years in Practice
Chapter 23
Yiyu Yao, Zhongzhi Shi, Yingxu Wang, Witold Kinsner, Yixin Zhong, Guoyin Wang
Cognitive informatics (CI) is a cutting-edge and multidisciplinary research area that tackles the fundamental problems shared by modern informatics... Sample PDF
Toward Cognitive Informatics and Cognitive Computers: A Report on IEEE ICCI'06
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