A survey of knowledge acquisition techniques and tools

Boose, John H.

doi:10.1016/s1042-8143(89)80003-2

Cited by 179 publications

(75 citation statements)

References 58 publications

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“…Our comparison of different approaches in knowledge engineering is not the first one. An early attempt to survey and classify most approaches in knowledge acquisition was given by [Boo88]. Methodologically close in spirit to this work are [KLV90], [NPB+91], [Lin93], and [FeH94].…”

Section: Introductionmentioning

confidence: 83%

A comparison of two approaches to model-based knowledge acquisition

Fensel¹,

Poeck

1994

Lecture Notes in Computer Science

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Abstract. This paper discusses and compares two different approaches to model-based knowledge acquisition. That is, we regard the Model-based and Incremental Knowledge Engineering (MIKE) approach and the Configurable Role-limiting Method approach (CRLM). MIKE is based on the distinction of different phases in the software development process. It uses the formal and operational knowledge specification language KARL allowing a precise and unique description of a model of expertise which is the outcome of the analysis phase. CRLM is based on the rolelimiting method approach. Role limiting shells are implementations of strong problem-solving methods and substantially simplify knowledge acquisition through guidance by predefined models of problem-solving and by sophisticated graphical user interfaces. The main disadvantages, namely inflexibility and brittleness, are to some degree overcome by the CRLM where the shell's problem-solving methods are split into smaller parts, which can then be reconfigured allowing the integration of new methods or other method combinations. Although these two approaches are often discussed as contradictory, we, however, experienced that both approaches complete each other very well. As an outcome of our comparison, we outline topics of future research for both approaches.

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Section: Introductionmentioning

confidence: 83%

A comparison of two approaches to model-based knowledge acquisition

Fensel¹,

Poeck

1994

Lecture Notes in Computer Science

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“…A domain expert needs to identify which information is mandatory. Knowledge acquisition [1] techniques can be used to support this process. The domain of, for example, all legal cases involving juveniles, is very large.…”

Section: Completeness and Consistencymentioning

confidence: 99%

Towards automatic identification of completeness and consistency in digital dossiers

Warnier¹,

Brazier²,

Apistola³

et al. 2007

Proceedings of the 11th International Conference on Artificial Intelligence and Law

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The emergence of digital dossiers in Courts of Law presents new opportunities to streamline the criminal prosecution chain. This papers proposes the use of agent technology to support automatic verification of consistency and completeness of data in such dossiers. It sketches how agent systems in combination with other AI technology, can be used to enforce consistency and completeness in digital dossiers in the context of the semi-open environment of the Courts.

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“…More comprehensive survey of knowledge acquisition systems, knowledge acquisition methodology, or systems for developing ontologies can be found in other work (Boose, 1989;Denny, 2002;Schreiber et al, 1999;Shadbolt et al, 1993;Sure et al, 2002).…”

Section: Knowledgementioning

confidence: 99%

Incorporating tutoring principles into interactive knowledge acquisition

Kim¹,

Gil²

2007

International Journal of Human-Computer Studies

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This paper argues that interactive knowledge acquisition systems would benefit from a tighter and more thorough incorporation of tutoring and learning principles. Current acquisition systems learn from users in a passive manner, and could instead be designed to incorporate the proactive capabilities that one expects of a good student. We first describe our analysis of the literature on teacher-student interaction and present a compilation of tutoring and learning principles that are relevant to interactive knowledge acquisition systems. We then point out what tutoring and learning principles have been used to date in the acquisition literature, though unintentionally and implicitly, and discuss how a more thorough and explicit representation of these principles would help improve how computers learn from users. We present our design and an initial implementation of an acquisition dialogue system called SLICK that represents acquisition principles and goals explicitly and declaratively, making the system actively reason about various acquisition tasks and generate its interactions dynamically. Finally, we discuss promising directions in designing acquisition systems by structuring interactions with users according to tutoring and learning principles.

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A survey of knowledge acquisition techniques and tools

Cited by 179 publications

References 58 publications

A comparison of two approaches to model-based knowledge acquisition

A comparison of two approaches to model-based knowledge acquisition

Towards automatic identification of completeness and consistency in digital dossiers

Incorporating tutoring principles into interactive knowledge acquisition

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