The Experts below are selected from a list of 288 Experts worldwide ranked by ideXlab platform
Guus Schreiber - One of the best experts on this subject based on the ideXlab platform.
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Chapter 25 Knowledge Engineering
Handbook of Knowledge Representation, 2008Co-Authors: Guus SchreiberAbstract:Publisher Summary The discipline of Knowledge Engineering grew out of the early work on expert systems in the seventies. With the growing popularity of Knowledge-based systems, there arose also a need for a systematic approach for building such systems, similar to methodologies in mainstream software Engineering. Over the years, the discipline of Knowledge Engineering has evolved into the development of theory, methods, and tools for developing Knowledge-intensive applications. In other words, it provides guidance about when and how to apply particular Knowledge-presentation techniques for solving particular problems. This chapter discusses a number of principles that have become the baseline of modern Knowledge Engineering. These include the common distinction made in Knowledge Engineering between task Knowledge and domain Knowledge. It explores the notion of problem-solving tasks in detail and presents typical patterns and methods for solving such tasks. It focuses on the domain perspective, in particular the representation and use of ontologies and discusses the main techniques that are being used in Knowledge Engineering.
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Knowledge Engineering and management
2000Co-Authors: Guus Schreiber, Hans Akkermans, Anjo Anjewierden, R. De Hoog, Nigel Shadbolt, B.j. WielingaAbstract:The disciplines of Knowledge Engineering and Knowledge management are closely tied. Knowledge Engineering deals with the development of information systems in which Knowledge and reasoning play pivotal roles. Knowledge management, a newly developed field at the intersection of computer science and management, deals with Knowledge as a key resource in modern organizations. Managing Knowledge within an organization is inconceivable without the use of advanced information systems; the design and implementation of such systems pose great organization as well as technical challenges. The book covers in an integrated fashion the complete route from corporate Knowledge management, through Knowledge analysis and Engineering, to the design and implementation of Knowledge-intensive information systems. The CommonKADS methodology, developed over the last decade by an industry-university consortium led by the authors, is used throughout the book. CommonKADS makes as much use as possible of the new UML notation standard. Beyond information systems applications, all software Engineering and computer systems projects in which Knowledge plays an important role stand to benefit from the CommonKADS methodology.
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AAAI Spring Symposium: Symbiotic Relationships between Semantic Web and Knowledge Engineering - Principles for Knowledge Engineering on the Web
Knowledge Engineering: Practice and Patterns, 1Co-Authors: Guus SchreiberAbstract:With the advent of the Web and the efforts towards a Semantic Web the nature of Knowledge Engineering has changed drastically. The new generation of Knowledge systems has left the closed world of isolated applications and feeds on the heterogeneous Knowledge sources available online. We propose principles for a new style of Knowledge Engineering on a Web scale. We illustrate these principles with examples from our efforts in developing a Semantic Web application targeted at large-scale cross-collection search in virtual cultural-heritage collections.
Dieter Fensel - One of the best experts on this subject based on the ideXlab platform.
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Situation and perspective of Knowledge Engineering
2000Co-Authors: Rudi Studer, Stefan Decker, Dieter Fensel, Steffen StaabAbstract:Knowledge Engineering was in the past primarily concerned with building and developing Knowledge-based systems, an objective which puts Knowledge Engineering in a niche of the world-wide research efforts at best. This has changed dramatically: Knowledge Engineering is now a key technology in the upcoming Knowledge society. Companies are recognizing Knowledge as their key assets, which have to be exploited and protected in a fast changing, global and competitive economy. This situation has led to the application of Knowledge Engineering techniques in Knowledge Management. The demand for more efficient (business to) business processes requires the interconnection and interoperation of different information systems. But information access and integration is not an algorithmic task that is easy to solve: much Knowledge is required to resolve the semantic differences of data residing in two information systems. Thus Knowledge Engineering has become a major technique for information integration. And, last but not least the fast growing World Wide Web generates an ever-increasing demand for more efficient Knowledge exploitation and creation techniques. Here again Knowledge Engineering technologies may become the key technology for solving the problem. In this paper we discuss these recent developments and describe our view of the future.
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XPS - Knowledge Engineering: Survey and Future Directions
XPS-99: Knowledge-Based Systems. Survey and Future Directions, 1999Co-Authors: Rudi Studer, Stefan Decker, Dieter Fensel, V. Richard BenjaminsAbstract:This paper provides an overview of important developments in the field of Knowledge Engineering. We discuss the paradigm shift from a transfer to a modeling approach and discuss two prominent methodological achievements: problem-solving methods and ontologies. To illustrate these and additional concepts we outline several modeling frameworks: CommonKADS, MIKE, PROTEGE-II, and D3. We also discuss two fields which have emerged in the last few years and are promising areas for applying and further developing concepts and methods from Knowledge Engineering: Intelligent Information Integration and Knowledge Management.
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Knowledge Engineering: principles and methods
1998Co-Authors: Rudi Studer, V. Richard Benjamins, Dieter FenselAbstract:This paper gives an overview of the development of the field of Knowledge Engineering over the last 15 years. We discuss the paradigm shift from a transfer view to a modeling view and describe two approaches which considerably shaped research in Knowledge Engineering: Role-limiting Methods and Generic Tasks. To illustrate various concepts and methods which evolved in recent years we describe three modeling frameworks: CommonKADS, MIKE and PROTEGE-II. This description is supplemented by discussing some important methodological developments in more detail: specification languages for Knowledge-based systems, problem-solving methods and ontologies. We conclude by outlining the relationship of Knowledge Engineering to Software Engineering, Information Integration and Knowledge Management.
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Formal Methods in Knowledge Engineering
The Knowledge Engineering Review, 1995Co-Authors: F.a.h. Van Harmelen, Dieter FenselAbstract:This paper presents a general discussion of the role of formal methods in Knowledge Engineering. We give an historical account of the development of the field of Knowledge Engineering towards the use of formal methods. Subsequently, we discuss the pro's and cons of formal methods. We do this by summarising the proclaimed advantages, and by arguing against some of the commonly heard objections against formal methods. We briefly summarise the current state of the art and discuss the most important directions that future research in this field should take. This paper presents a general setting for the other contributions in this issue of the Journal, which each deal with a specific issue in more detail.
Wendy A. Rogers - One of the best experts on this subject based on the ideXlab platform.
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The smooth (tractor) operator: insights of Knowledge Engineering.
Applied ergonomics, 2012Co-Authors: Ralph H. Cullen, Cory-ann Smarr, Daniel Serrano-baquero, Sara E. Mcbride, Jenay M. Beer, Wendy A. RogersAbstract:The design of and training for complex systems requires in-depth understanding of task demands imposed on users. In this project, we used the Knowledge Engineering approach (Bowles et al., 2004) to assess the task of mowing in a citrus grove. Knowledge Engineering is divided into four phases: (1) Establish goals. We defined specific goals based on the stakeholders involved. The main goal was to identify operator demands to support improvement of the system. (2) Create a working model of the system. We reviewed product literature, analyzed the system, and conducted expert interviews. (3) Extract Knowledge. We interviewed tractor operators to understand their Knowledge base. (4) Structure Knowledge. We analyzed and organized operator Knowledge to inform project goals. We categorized the information and developed diagrams to display the Knowledge effectively. This project illustrates the benefits of Knowledge Engineering as a qualitative research method to inform technology design and training.
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Knowledge Engineering: Applying the Process:
Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2004Co-Authors: C. Travis Bowles, Julian Sanchez, Arthur D. Fisk, Wendy A. RogersAbstract:Current expert operators provide valuable information to developers of new systems. They can help inform the design of new systems, new interfaces, or new methods of training. Knowledge Engineering...
Nigel Shadbolt - One of the best experts on this subject based on the ideXlab platform.
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Knowledge Engineering and psychology: Towards a closer relationship
International Journal of Human-Computer Studies, 2006Co-Authors: Nick Milton, David D. Clarke, Nigel ShadboltAbstract:Knowledge Engineering projects deal with a wide range of domains within organizational and academic contexts. A number of elicitation techniques are used to acquire Knowledge from experts. Most of these techniques originated within psychology but have been developed by Knowledge engineers to become more structured, efficient and systematic. Until now, nobody has tried to re-apply these modified techniques back into psychology. This paper describes work that addresses this matter. It focuses on the psychological Knowledge possessed by all people that enables them to deal with everyday problems and make life decisions. We refer to this as 'personal Knowledge'. To take a Knowledge Engineering approach to personal Knowledge, we investigated the use of Knowledge elicitation techniques to capture personal Knowledge. We describe an empirical study involving ten participants and 80 Knowledge acquisition sessions that assessed eight elicitation techniques in this context. The results revealed that each of the techniques showed promise at efficiently capturing and structuring aspects of an individual's personal Knowledge. A content analysis of the acquired Knowledge led to the construction of a meta-model (a primitive ontology) of personal Knowledge and to the design for a new methodology for psychological research. From the perspective of psychology, the paper shows that Knowledge Engineering methods can be of value to psychologists. From the perspective of Knowledge Engineering and the wider computer science community, the paper shows that empirical methods used by psychologists can benefit the development and evaluation of ontologies and elicitation techniques.
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Knowledge Engineering and management
2000Co-Authors: Guus Schreiber, Hans Akkermans, Anjo Anjewierden, R. De Hoog, Nigel Shadbolt, B.j. WielingaAbstract:The disciplines of Knowledge Engineering and Knowledge management are closely tied. Knowledge Engineering deals with the development of information systems in which Knowledge and reasoning play pivotal roles. Knowledge management, a newly developed field at the intersection of computer science and management, deals with Knowledge as a key resource in modern organizations. Managing Knowledge within an organization is inconceivable without the use of advanced information systems; the design and implementation of such systems pose great organization as well as technical challenges. The book covers in an integrated fashion the complete route from corporate Knowledge management, through Knowledge analysis and Engineering, to the design and implementation of Knowledge-intensive information systems. The CommonKADS methodology, developed over the last decade by an industry-university consortium led by the authors, is used throughout the book. CommonKADS makes as much use as possible of the new UML notation standard. Beyond information systems applications, all software Engineering and computer systems projects in which Knowledge plays an important role stand to benefit from the CommonKADS methodology.
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Constructive Knowledge Engineering
Knowledge-Based Systems, 1995Co-Authors: Mark Elliot, Nigel Shadbolt, H. T. Bull, C. I. Pulford, W. SmithAbstract:Knowledge Engineering research has shifted from the transfer, expert-driven metaphor for system development to an interpretative, model-driven metaphor. The paper examines the relationship between these two metaphors and introduces a third: constructive, problem-driven Engineering. The paper postulates a theoretical construction of the interrelationships between these in terms of the epistemology of expertise, the Engineering process, and representational issues. The constructive approach is further illuminated with example systems, and a set of guidelines for applying the constructive approach is provided.
Jay Liebowitz - One of the best experts on this subject based on the ideXlab platform.
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Knowledge Management: Learning from Knowledge Engineering
2019Co-Authors: Jay LiebowitzAbstract:From the Publisher: Knowledge Management (KM) is strongly rooted in the discipline of Knowledge Engineering (KE), which in turn grew partly out of the artificial intelligence field. Despite their close relationship, however, many KM specialists have failed to fully recognize the synergy or acKnowledge the power that KE methodologies, techniques, and tools hold for enhancing the state of the art in Knowledge Management.Knowledge Management: Learning from Knowledge Engineering addresses this vacuum. It gives concise, practical information and insights drawn from the author's many years of experience in the fields of expert systems and Knowledge Management. Based upon research, analyses, and illustrative case studies, this is the first book to integrate the theory and practice of artificial intelligence and expert systems with the current organizational and strategic aspects of Knowledge Management.The time has come for Knowledge Management professionals to appreciate the synergy between their work and the work of their counterparts in Knowledge Engineering. Knowledge Management: Learning from Knowledge Engineering is the ideal starting point for those in KM to learn from and exploit advances in that field, and thereby advance their own.
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The use of systems analysis tools in Knowledge Engineering
Journal of Computer Applications in Technology, 2014Co-Authors: Jay LiebowitzAbstract:'Expert systems' is an application which is still in its infancy. The process of building expert systems is known as 'Knowledge Engineering'. To improve the Knowledge Engineering approach, many lessons could be learned from the systems analysis/software Engineering field. Knowledge engineers could borrow some of the successful techniques used in software Engineering to solidify and enhance the expert system building process. This paper addresses these areas where software Engineering practices could greatly aid expert system development.
