Evaluation of verification tools for knowledge-based systems

Preece, Alun; Talbot, Stéphane; Vignollet, Laurence

doi:10.1006/ijhc.1997.0152

Cited by 18 publications

(13 citation statements)

References 8 publications

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“…The increasing complexity of recommender knowledge bases makes quality assurance a critical task [26] [31]. Mechanisms have to be provided which allow us to guarantee the correctness of • Constraint Certification.…”

Section: Cwadvisor Test Designermentioning

confidence: 99%

“…A study conducted in the restaurant recommendation domain found that users needed fewer interactions to find a good restaurant when using a personalized conversational recommender than when using a non-personalized one [39]. Both natural language interaction [39] and result visualization [24] [31]. The literature on test case generation in knowledge-based systems development is still largely directed at rule-based types of systems [31].…”

Section: The Role Of Knowledge Acquisition In Recommender Projectsmentioning

confidence: 99%

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An Integrated Environment for the Development of Knowledge-Based Recommender Applications

Felfernig

Friedrich

Jannach

et al. 2006

International Journal of Electronic Commerce

150

102

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Abstract. The complexity of product assortments offered by online selling platforms makes the selection of appropriate items a challenging task. Customers can differ significantly in their expertise and level of knowledge regarding such product assortments. Consequently, intelligent recommender systems are required which provide personalized dialogues supporting the customer in the product selection process. In this paper we present the domainindependent, knowledge-based recommender environment CWAdvisor which assists users by guaranteeing the consistency and appropriateness of solutions, by identifying additional selling opportunities, and by providing explanations for solutions. Using examples from different application domains, we show how model-based diagnosis, personalization, and intuitive knowledge acquisition techniques support the effective implementation of customer-oriented sales dialogues. In this context, we report our experiences gained in industrial projects and present an evaluation of successfully deployed recommender applications.

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Section: Cwadvisor Test Designermentioning

confidence: 99%

Section: The Role Of Knowledge Acquisition In Recommender Projectsmentioning

confidence: 99%

An Integrated Environment for the Development of Knowledge-Based Recommender Applications

Felfernig

Friedrich

Jannach

et al. 2006

International Journal of Electronic Commerce

150

102

View full text Add to dashboard Cite

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“…Based on our experience from the three projects described earlier and on best practices as described in literature (eg (Preece et al, 1997;Gupta, 1993;Tsai et al, 1999)) we have identified (and largely implemented) an architecture of tool support for the development of rule bases.…”

Section: Core Principlesmentioning

confidence: 99%

RULES AS SIMPLE WAY TO MODEL KNOWLEDGE - Closing the Gap between Promise and Reality

Zacharias¹

2008

Proceedings of the Tenth International Conference on Enterprise Information Systems

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Abstract:There is a considerable gap between the potential of rules bases to be a simpler way to formulate high level knowledge and the reality of tiresome and error prone rule bases creation processes. Based on the experience from three rule base creation projects this paper identifies reasons for this gap between promise and reality and proposes steps that can be taken to close it. An architecture for a complete support of rule base development is presented.

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“…A number of existing approaches to knowledge base verification can be found in [1], [5], [9], [14], [15], [16], [17], [18], [19], [21], [22], [23], [24], [25], [26], [28], [29], [30], [31], [32]; in [37] a comparative overview can be found for some of the existing approaches. As argued in [37], one of the problems in almost all of the existing approaches to knowledge base verification is that both the properties addressed (e.g., consistency, correctness, completeness) and the techniques used for verification strongly depend on the knowledge representation language in which the knowledge base is expressed.…”

Section: Syntactic Versus Semantic Perspective On Verificationmentioning

confidence: 99%

“…An automatic verifier can serve as a very useful tool during the development of a (knowledge-based) system. It makes it possible to check the knowledge, prior to testing by identifying, for example, inconsistency, incorrectness, redundancy, and missing knowledge, thus supporting the knowledge engineer in the knowledge elicitation and modelling process; e.g., [29].…”

Section: Introductionmentioning

confidence: 99%

A semantical perspective on verification of knowledge

Leemans

Treur

Willems

2002

Data & Knowledge Engineering

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The usual approaches to verification of knowledge in the literature have a rather practical nature, and are sometimes ad hoc and depending on the syntactical structure of a particular knowledge representation language in the definitions of the properties that are tested. In this paper a more principled investigation provides a well-defined semantical basis, independent of any particular syntactical representation, of the various properties that can be tested, and their logical relationships. Central in this unifying semantical framework is the notion of domain description and the forcing relation that is used as a standard for the inferences. Properties covered include consistency, three variants of soundness, three variants of completeness, empirically foundedness, and wellinformedness.

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Evaluation of verification tools for knowledge-based systems

Cited by 18 publications

References 8 publications

An Integrated Environment for the Development of Knowledge-Based Recommender Applications

An Integrated Environment for the Development of Knowledge-Based Recommender Applications

RULES AS SIMPLE WAY TO MODEL KNOWLEDGE - Closing the Gap between Promise and Reality

A semantical perspective on verification of knowledge

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