Knowledge Engineering

@inproceedings{Debenham1998KnowledgeE,
  title={Knowledge Engineering},
  author={John K. Debenham},
  booktitle={Artificial Intelligence},
  year={1998}
}
  • J. Debenham
  • Published in Artificial Intelligence 1998
  • Computer Science
Knowledge Engineering is the aspect of systems engineering which addresses uncertain process requirements by emphasising the acquisition of knowledge about a process and representing this knowledge in a Knowledge Based System. The discipline has moved from one producing small expert systems to one producing embedded KBS in larger computational solutions. This transition has resulted in the development of methodologies that guide the knowledge engineering of a product. The KADS methodology is… 

Knowledge Engineering

  • G. Schreiber
  • Computer Science
    Handbook of Knowledge Representation
  • 2008

Multi-perspective modelling for knowledge management and knowledge engineering

TLDR
An analytical framework originally intended for information systems architecture can be used to support knowledge management, knowledge engineering and the closely related discipline of ontology engineering, and can provide a guide to good selection of knowledge management techniques.

A Semantic Approach Towards Software Engineering of Knowledge Bases

TLDR
Pragati's Multi-ViewPoint-Clustering Analysis (MVP-CA) tool is a semi-automated tool allowing the user to focus attention on different aspects of the problem, thus providing a valuable aid for comprehension, maintenance, integration and evolution of knowledge-based systems.

Knowledge Engineering 1.1 Introduction 1.2 Baseline

  • Computer Science
TLDR
This chapter discusses a number of principles, that have become the baseline of modern knowledge engineering, and explores the notion of problem-solving tasks in detail and presents typical patterns and methods user for solving such tasks.

A Lesson for Software Engineering from Knowledge Engineering

TLDR
A unified model of knowledge represents business rules at a higher level of abstraction than the rule-based paradigm that enables any changes to business rules to be quantified and tracked through to the imperative programs that implement them.

Knowledge Elicitation Techniques in a Knowledge Management Context

TLDR
It is proposed that the special agent (analyst) might be needed to elicit knowledge from individuated individuals in knowledge management tasks.

A developed methodology for human driven knowledge acquisition

TLDR
A method for human driven knowledge acquisition (KA), which was performed in an Iranian petrochemical company, is developed with knowledge assessment as a significant and critical success factor.

Modelling Roles in Business Systems Using Role Objects

TLDR
An approach is created that aims to use the role concept for abstracting knowledge from a business system and uses role objects to represent the knowledge.
...

References

SHOWING 1-10 OF 142 REFERENCES

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TLDR
This article compares representative RA and KA techniques, which are grouped, according to elicitation mode, on three dimensions: communication obstacles, a technique's focus of control, and the nature of the understanding gained from using the technique.

A Comparison of Two Approaches to Model-Based Knowledge Acquisition

TLDR
This paper discusses and compares two different approaches to model-based knowledge acquisition and experiences that both approaches complete each other very well.

Knowledge acquisition or requirements analysis?

  • H. Sharp
  • Computer Science
    Proceedings of International Conference on Expert Systems for Development
  • 1994
TLDR
The use of the expertise model of the KADS method within requirements analysis, and the use of object-oriented analysis within knowledge acquisition are discussed.

The Design of a Knowledge-Based Decision Support System to Support the Information Analyst in Determining Requirements

TLDR
The conceptual design and development of a knowledge-based DSS to support information analysts in the critical decision task of determining requirements for the design of effective information systems is discussed.

Issues in knowledge level modelling

TLDR
This chapter shows how the knowledge level changed the authors' views on what knowledge systems are and how the problems with first generation expert systems might be overcome.

A comparison of languages which operationalize and formalize KADS models of expertise

TLDR
This paper describes eight formal languages for KADS models of expertise, and compares these languages with respect to their modelling primitives, their semantics, their implementations and their applications, to enable a meaningful comparison.

Design and implementation of a knowledge-based decision support system for estimating software development work-effort

TLDR
A Decision Support System for estimating the work-effort is designed, in which the processing of the qualitative data is made by an expert system while a function points analysis provides the theoretical work- Effort according to the type of software and the past experience.

Facilitating the Development of Knowledge Based Systems, A Critical Review of Aquisition Tools and Techniques

TLDR
It is argued that there is a strong case for a preliminary knowledge analysis or domain phase of KBS development, which facilitates subsequent design, development and maintenance phases.

Information/Knowledge Acquisition Methods for Decision Support Systems and Expert Systems

Constructing the Functional Model

TLDR
The problem of deriving the best functional model for knowledge-based systems is shown to be NP-complete and a sub-optimal algorithm for deriving solutions to the functional model construction problem is given.
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