- •М инистерство образования и науки РоссиЙской Федерации южно-уральский государственный университет
- •Шакурова г. Л.
- •Челябинск
- •Введение
- •Контрольная работа №1
- •Часть I
- •Контрольная работа №2
- •Часть I
- •Часть I
- •Контрольная работа №4
- •Часть I
- •Контрольная работа №5
- •Часть I
- •Контрольная работа №3
- •Types of Partnerships
- •Active Vocabulary
- •Comprehension Questions
- •Vocabulary Practice
- •Grammar exercises
- •Business Corporations
- •Active Vocabulary
- •Comprehension Questions
- •Vocabulary Practice
- •Grammar exercises
- •Executive’s Functions Dialogue
- •Active Vocabulary
- •Vocabulary exercises
- •Grammar exercises
- •Organization Structure
- •Active Vocabulary
- •Comprehension Questions
- •Vocabulary Practice
- •Grammar exercises
- •Business and Banks
- •Active Vocabulary
- •Vocabulary Practice
- •Grammar exercises
- •Marketing
- •Active Vocabulary
- •Comprehension Questions
- •Vocabulary Practice
- •Dialogue
- •Active Vocabulary
- •Vocabulary Practice
- •Active Vocabulary
- •Family Dialogue
- •Семья Диалог
- •My visiting card Dialogue
- •Моя визитная карточка Диалог
- •Education Dialogue
- •Образование Диалог
- •Learning Foreign Languages Dialogue
- •Изучаем иностранные языки Диалог
- •Choosing a Career Dialogue
- •Выбор профессии Диалог
- •Young People’s Problems Dialogue
- •Проблемы молодёжи Диалог
- •1 Work and jobs
- •2 Ways of working
- •Old and new ways
- •3 Recruitment and selection
- •Recruitment
- •Applying for a job
- •Selection procedures
- •3.1 Complete the crossword. Use appropriate forms of words from а, в and с opposite.
- •3.2 How divide die words in 3.1 into two groups:
- •3.3 Replace the underlined phrases with correct forms of words and expressions from a. В and с opposite.
- •Information Integration for Concurrent Engineering
- •Idef5 Method Report
- •1 Executive Summary
- •1.1 Motivations
- •1.1.1 Motivations for Ontology
- •1.1.2 Motivations for an Ontology Development Method
- •1.2 Benefits of Ontology Development
- •1.3 Overview of the Report
- •1.4 The Connection Between idef5 and Other Methods
- •2 Conceptual Foundations of Ontology
- •2.1 The Nature of Ontological Inquiry
- •Contents
- •1.Чехович д.Р. Английский язык для менеджеров . Ч.1 Челябинск 2008г.
- •2.Антонов о.И. Английский язык для бизнесмена. Москва 2010г.
- •3.К.Дж. Поллак Ролевые и ситуативные диалоги. Санкт-Петербург 2010г.
1.1.1 Motivations for Ontology
The ability to fix a domain vocabulary and its meaning in the context of use in this manner is
critical for true concurrent engineering. A large engineering or manufacturing project involves
the resources of many different clusters of cooperative agents (human or otherwise) in the given
endeavor. Each cluster makes its own contributions, and the overall success of the project
depends in large measure on the degree of integration between those different clusters throughout
the development process. A key to effective integration is the accessibility of rich ontologies
characterizing each of the domains addressed by each cluster. For instance, access to a
manufacturing ontology that includes constraints on how a given part is manufactured can aid
designers in their design of a complex product by giving them insight into the manufacturing
implications of their design concepts. Similarly, access to an engineering ontology that includes
constraints on how a given part is to function given a particular shape or fit can aid process
planners in their development of the appropriate manufacturing processes. A commonly
accessible collection of relevant ontologies thus permits more efficient sharing of information
arising from various sources within the enterprise.
A related motivation for ontology capture is the standardization of terminology. An enormous
problem in the coordination of large projects is the diversity of backgrounds the various kinds of
engineers bring to their respective roles. As a consequence, many engineers use similar
terminology in many different ways with many different connotations. Because of such
differences, the information that one engineer intends to convey to another may in fact become
garbled; in the best case, such miscommunications can be responsible for a great deal of lost time
and resources; in the worst case, such miscommunications can result in the loss of life.
Consequently, it is often necessary in the course of a large project to standardize the relevant
vocabulary. The ontology capture method provides a principled method for carrying out this task
efficiently and effectively, and maintaining the results of the task in a robust, accessible form.
This suggests another, strong motivation for ontology: reusability. Among the most significant
problems in engineering and manufacturing in general is the redundant effort expended in
capturing or recreating information that has already been recorded elsewhere. For example, in
programming, the same kinds of routines (e.g., in the design of user interfaces) are often used
again and again in different programs by (in general) different programmers. Consequently,
enormous amounts of time and effort have gone into reinventing the wheel time and again.
Recognition of this problem has led to the development of vast libraries that contain often used
routines which programmers can simply call straight into their programs, rather than having to
duplicate the function of existing code. Engineering and manufacturing face the same type of
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problem. Manufacturing domains, for example, share many common features that are
independent of the specific characteristics of a given domain; and the more similar the domains,
the more such features they share. Rather than encoding this information all over again in every
new setting, analogous of the concept of a programming library, one can imagine collecting this
common information into ontology libraries, (i.e., large revisable ontology databases of
structured, domain-specific information.) Information in these ontologies can then be reused and
modified to suit the needs of the moment. Moreover, because ontologies provide a standardized
terminology by their very nature, no special additional effort need be expended on fixing domain
terminology. It must be emphasized, however, that, despite the potential size of a given
ontology, the concept itself is highly scaleable; that is, ontologies are no less effective in smaller
contexts than on very large ones.
