After characterizing the main tasks of medical and ecological expert system the paper presents certain problems in knowledge acqusition automation, such as identification of patient states and nature phenomenas or their description followed by formalization and coding. The general rules of knowledge acquisition automation are first presented and then reffered to medical and ecological expert systems. The final remarks cover the comparison of the ideas presented, their implementability and utilizability.
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The curse of dimensionality is a damning factor for numerous potentially powerful machine learning techniques. Widely approved and otherwise elegant methodologies used for a number of different tasks ranging from classification to function approximation exhibit relatively high computational complexity with respect to dimensionality. This limits severely the applicability of such techniques to real world problems. Rough set theory is a formal methodology that can be employed to reduce the dimensionality of datasets as a preprocessing step to training a learning system on the data. This paper investigates the utility of the Rough Set Attribute Reduction (RSAR) technique to both supervised and unsupervised learning in an effort to probe RSAR's generality. FuREAP, a Fuzzy-Rough Estimator of Algae Populations, which is an existing integration of RSAR and a fuzzy Rule Induction Algorithm (RIA), is used as an example of a supervised learning system with dimensionality reduction capabilities. A similar framework integrating the Multivariate Adaptive Regression Splines (MARS) approach and RSAR is taken to represent unsupervised learning systems. The paper describes the three techniques in question, discusses how RSAR can be employed with a supervised or an unsupervised system, and uses experimental results to draw conclusions on the relative success of the two integration efforts.
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In spite of their industrial success, the development of intelligent systems is still a complex and risky task. When building intelligent systems, we see that domain knowledge is often present at different levels of formalization-ranging from text documents to explicit rules. In this paper, we describe the knowledge formalization continuum as a metaphor to help domain specialists during the knowledge acquisition phase. To make use of the knowledge formalization continuum, the agile use of knowledge representations within a knowledge engineering project is proposed, as well as transitions between the different representations, when required. We show that a semantic wiki is a flexible tool for engineering knowledge on the knowledge formalization continuum. Case studies are taken from one industrial and one academic project, and they illustrate the applicability and benefits of semantic wikis in combination with the knowledge formalization continuum.
A Context Search algorithm used for lexical knowledge acquisition is presented. Knowledge representation based on psycholinguistic theories of cognitive processes allows for implementation of a computational model of semantic memory in the form of semantic network. Knowledge acquisition using supervised dialog templates have been performed in a word game designed to guess the concept a human user is thinking about. The game that has been implemented on a web server, demonstrates elementary linguistic competencies based on lexical knowledge stored in semantic memory, enabling at the same time acquisition and validation of knowledge. Possible applications of the algorithm in domains of medical diagnosis and information retrieval are sketched.
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In the paper, learning system of manufacturing knowledge acquisition in the range of tool selection to machining operation was presented. Currently often and often research concerning machine learning is developed. Machine learning includes issues of system designing which improves its operations, along with analysis of experience represented by file of learning examples. Recently, machine learning methods have been applied successfully in many practical problems and are becoming a part of advanced information systems - in particular concerning knowledge discovery in databases, and the so-called data mining. Learning system acquires knowledge by using the method of decision trees induction. This method allows approximation of classification functions of discrete output values relating to certain terms, decision classes. The system learns to select tools on the basis of the ones chosen out of the database by process engineering experts for machining operations. In the method of decision trees induction a decision tree is created. This tree allows classification of the whole learning examples file into homogeneous classes. On the basis of the decision tree, decision rules are created. Next, these rules are used in expert system for selection of tools from outside of the file of learning examples.
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Knowledge engineering becomes more frequently seen in engineering practice. The possibility of developing an application which is able to increase speed and quality of for example designers’ work is invaluable useful. The main task of Knowledge Based Engineering (KBE) is providing new and intelligent applications to support routine engineering work tasks. To prepare such kind of application it is necessary to go through the procedure of knowledge acquisition and knowledge postprocessing. This process requires a lot of a knowledge engineer work and experience. In this paper authors shown the main things related to knowledge acquisition process in the context of a Generative Model development. Additionally, some new approach to knowledge storage with Microsoft Excel is presented.
This study analyzes the interplay between the characteristics of business strategy implementations and their interaction with different types of knowledge in the manufacturing enterprise. It is based on a survey and data obtained from 119 Polish manufacturing enterprises. This study is innovative in applying the Group Method of Data Handling to the business context. This article develops a framework of achieving the strategy’s goals in terms of the types of knowledge acquisition in a manufacturing company, and further discusses the research results.
The diagnostics of machinery is nowadays aided by expert systems which require knowledge on the machine to be diagnosed. This knowledge may be acquired either from human experts or from databases containing examples. The paper deals with several methods of knowledge acquisition from examples. It addresses the whole range of problems starting from preparation of examples up to the verification and validation of the knowledge base which ends the proceeding. To acquire diagnostic knowledge from a dataset of examples we apply machine learning or knowledge discovery methods. We also describe a new method suitable for induction of rules which is especially useful in technical diagnostic of machinery where complex structure of a set of technical states often occurs. An example of the application of described methods for acquisition of diagnostic knowledge on rotating machinery is given, too.
This paper summarizes our preliminary experiences with implementing some of the ideas lying behind the concept of creative environment. Research group at the National Institute of Telecommunications has developed a prototype framework for collaborative knowledge acquisition and sharing, called PrOnto. At the moment the artifacts we organize and share are typical sources of scientific knowledge, namely journal papers and web pages. In PrOnto we introduce two interrelated explicit levels of knowledge representation: keywords and ontological concepts. Each user of the framework maintains his own ontological profile, consisting of concepts and each concept is, in turn, by subjective user's decision, related to a set of weighted keywords that define its meaning. Furthermore, dedicated indexing engine is responsible for objectively establishing correspondence between documents and keywords, or in other words, the measure of representativeness of the keyword to document's content. Developing an appropriate knowledge model is a preliminary step to share it efficiently. We believe that higher level representation facilitates exploration of other people's areas of interest. PrOnto gives an opportunity to browse knowledge artifacts from the conceptual point of view of any user registered in the system. The paper presents the ideas behind the PrOnto framework, gives an outline of its components and finalizes with a number of conclusions and proposals for futuren enhancements.
The paper is aimed at presenting a model of transition from quality management systems to knowledge management systems in software developing organizations. The methodology focuses on presenting components of the model of transition from quality management systems to knowledge management systems. The paper defines the model of transition from the quality management systems conformable with series 9000 ISO international standards supplemented with ISO/IEC 90003:2004 to knowledge management systems. The model consists of four processes and five elements treated as the results of the process implementation. The paper demonstrates how quality system elements can be useful in the implementation of knowledge management system processes, such as localisation and the acquisition of knowledge.
There are many factors that influence the scope and assimilation of knowledge that textbooks contain. Among the most important ones there are strategic places, the content of which contributes to the contextualization of utterance and on which the attention of the audience is focused. The position of text opening, that is the title or initial sentence, can be mentioned among them. The influence of the form of headlines and the contents on the effectiveness of the acquisition of knowledge from the texts is a result of cognitive processes, activated during the process of reading. It appears from the results of the research, that the application of full-thematic titles improves the effects of working with text. Headlines of that type control the cognitive energy in a better way and initiate more adequate cognitive schemata that allow for a more complete integration of received information. The influence of the title brings expected results, when it is reinforced by a suitable method of working with the text.
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The paper deals with a comprehensive methodology concerning knowledge acquisition on machinery for the purpose of expert systems suitable for aiding of diagnostic inference. The methodology includes selected methods of diagnostic knowledge representation, methods of knowledge acquisition from domain experts and from previously classified examples, methods of assessment of previously acquired knowledge and a scenario of knowldge acquisition process. All the methods have been implemented in a Knowledge Acquisition System. Moreover, some examples of applications of the elaborated methodology have been given.
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As a special way in which the human brain is learning new knowledge, incremental learning is an important topic in AI. It is an object of many AI researchers to find an algorithm that can learn new knowledge quickly, based on original knowledge learned before, and in such way that the knowledge it acquires is efficient in real use. In this paper, we develop a rough set and rule tree based incremental knowledge acquisition algorithm. It can learn from a domain data set incrementally. Our simulation results show that our algorithm can learn more quickly than classical rough set based knowledge acquisition algorithms, and the performance of knowledge learned by our algorithm can be the same as or even better than classical rough set based knowledge acquisition algorithms. Besides, the simulation results also show that our algorithm outperforms ID4 in many aspects.
W pracy przedstawiono pewną koncepcję systemu ekspertowego o nazwie LOGISTER wspomagającego organizację pracy firmy spedycyjnej. Podano podstawowe założenia budowy oraz opis funkcjonowania tego systemu. Wiedza zgromadzona w bazie wiedzy systemu reprezentowana jest przez reguły i fakty, a pozyskiwanie wiedzy odbywa się na bieżąco w trakcie pracy systemu. Zrealizowana została implementacja komputerowa zaproponowanego systemu LOGISTER i zaprezentowano przebieg procesu wnioskowania.
EN
This paper presents certain concept of expert system, called LOGISTER, which supports work organization in logistic company. Fundamental establishments of systems construction and functional description of that system is presented. Knowledge gathered in systems knowledge base is formed as rules and facts and its acquisition is ad hoc operation. Implementation of presented LOGISTER system was accomplished and course of reasonning process was considered.
Knowledge acquisition from experts is a costly and time-consuming task. While domain experts have the necessary knowledge and expertise, they rarely have the experience needed to translate this knowledge into the model. This paper describes typical problems that are encountered by knowledge engineers when building Bayesian network models and illustrates some practical techniques to overcome them. The presented examples capture the problems that occurred during elicitation the numerical parameters of the model for diagnosis of liver disorders.
PL
Pozyskiwanie wiedzy od ekspertów jest kosztownym i czasochłonnym zadaniem. Pomimo ogromnej wiedzy i doświadczenia, jakie posiadają eksperci, niejednokrotnie nie potrafią ich przenieść na tworzony model. Poniższy artykuł opisuje przykłady problemów, z jakimi może się zetknąć inżynier wiedzy w trakcie budowania modeli sieci bayesowskich, jak również proponuje rozwiązania tych problemów. Prezentowane przykłady dotyczą problemów, jakie pojawiły się w trakcie pozyskiwania od eksperta parametrów numerycznych modelu sieci bayesowskiej do diagnozowania chorób wątroby.
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W artykule omówiono podstawowe zasady funkcjonowania systemów Poszerzonej Rzeczywistości, a także możliwości praktycznego wykorzystania w procesach zdobywania i organizowania wiedzy implementowanych między innymi we współczesnych strukturach wojskowych.
EN
This paper surveys the basic concepts of Augmented Reality, it’s capabilities and practical examples of implementation in knowledge acquisition and organization processes which are used, inter alia, in contemporary army structures.
The paper deals with an outline of a methodology of acquisition of diagnostic knowledge concerning machinery and processes. The problem formulated in the paper concerns the possibility to develop a general methodology of acquiring knowledge of technical diagnostics. Two issues are discussed: methods of knowledge acquisition about machinery mainly considered as static objects, and methods of acquiring knowledge in the form of heuristic models of dynamic objects and processes. Methods developed in the author's research group are mainly dealt with. The paper concludes with future work envisaged.
PL
W artykule przedstawiono zarys metodyki pozyskiwania wiedzy diagnostycznej dotyczącej maszyn i procesów. Sformułowany problem badawczy dotyczy możliwości utworzenia i rozwoju ogólnej metodologii pozyskiwania wiedzy w zakresie diagnostyki technicznej. Omawiane są dwie grupy metod: pozyskiwania wiedzy o maszynach interpretowanych jako obiekty statyczne, a także pozyskiwania wiedzy w postaci heurystycznych modeli obiektów (dynamicznych) oraz procesów. Przedmiotem opisu SA w głównej mierze metody rozwijane w grupie badawczej, w której pracach uczestniczy autor. Artykuł zakończony jest próba określenia przyszłych prac dotyczących pozyskiwania wiedzy diagnostycznej.
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The paper deals with selected problems of knowledge acquisition for intelligent information systems that may be applied for aiding technical diagnostics of machinery and equipment. Two main kinds of knowledge are discussed, i.e. declarative and procedural knowledge. Some methods of declarative knowledge acquisition from domain experts and from databases are presented, the latter being divided into machine learning methods and knowledge discovery ones. Examples of declarative knowledge acquisition and discovery from databases are shown. Moreover, an example of procedural knowledge acquisition from a domain expert is presented. The paper concludes with new issues of knowledge acquisition methodology.
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Pozyskiwanie wiedzy stało się obecnie bardzo ważnym zagadnieniem, zarówno w dziedzinie projektowania jak i eksploatacji maszyn. Od wielu lat w Katedrze Podstaw Konstrukcji Maszyn Politechniki Śląskiej prowadzone są badania w tym zakresie, które zaowocowały opracowaniem metodologii pozyskiwania wiedzy w projektowaniu i eksploatacji maszyn. Wiedza pozyskiwana może być zarówno od specjalistów, ale także z baz danych. W przypadku pozyskiwania wiedzy z baz danych prowadzone są prace w zakresie uczenia maszynowego jak również automatycznego odkrywania zależności w bazach danych - tzw. odkrywania wiedzy. Aby proces pozyskiwania był bardziej efektywny, a pozyskana wiedza była uogólniona, dane zgromadzone w bazach danych przetwarza się wstępnie, redukując ilość informacji, którą niosą dane, a także dokonując selekcji tej informacji. Pozyskana wiedza może zostać zastosowana w bazach wiedzy systemów doradczych, nad rozwojem których również prowadzone są prace. Jednym z osiągnięć jest także opracowanie metodologii diagnozowania złożonych obiektów technicznych z zastosowaniem tzw. odwrotnych modeli diagnostycznych. W referacie przedstawiono najważniejsze osiągnięcia Katedry Podstaw Konstrukcji Maszyn w ramach omawianych zagadnień.
EN
Knowledge acquisition becomes an important problem in both machinery design and exploitation domains. In the Department of Fundamentals of Machinery Design, Silesian University of Technology, from many years there has been conducted research concerning the problem. As a result of the research a methodology of knowledge acquisition in the design and exploitation of machinery was developed. Knowledge could be acquired from two sources: experts and databases. In the case of knowledge acquisition from databases, there are conducted researches concerning machine learning methods and also the so-called knowledge discovery - discovery of dependencies in databases. To make the knowledge acquisition process more effective, and to make the knowledge more general, the collected data are preprocessed. The preprocessing consist of two main stages: a reduction of information and its selection. The acquired knowledge could then be used in a knowledge base of an expert system. One of the achievements is also the development of a methodology of diagnosis of complex technical object using inverse diagnostic models. In the paper there are presented the most important achievements of the Department of Fundamentals of Machinery Design within a framework of above mentioned problems.
Badanie wiedzy jako pewnej wyodrębnionej dziedziny zarządzania przypada na ostatnie dziesięciolecia. Jednym z ważniejszych wniosków wynikających z tych badań jest konstatacja, że współczesna gospodarka nie zmierza do pozyskiwania wiedzy, a pozyskiwania umiejętności. Tymczasem współczesne uniwersytety dostarczają raczej wiedzę, a nie umiejętności.
EN
The last ten years this the time of the research of the knowledge, the distinctly defined field of science. One of the major conclusions formulated based on this research is that, the modern economy is not aimed at knowledge acquisition but rather at the acquiring of the competencies, whereas the modern universities deliver mostly knowledge not the competencies.
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