TheCommonKADS library in perspective

Valente, André; Breuker, Joost; Velde, Walter Van de

doi:10.1006/ijhc.1998.0212

Cited by 15 publications

(11 citation statements)

References 33 publications

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“…This study addresses that problem by building models for individual applications using CommonKADS models. It should however be noted that many of the components of these models can be reused (Valente, 1998). The aim of producing an organisation model is to establish the objectives of the system in terms of the organisation (De Hoog, 1994).…”

Section: Figure 3 Composite Image Of Part Of the Study Area Around Dementioning

confidence: 99%

“…The CommonKADS Expertise Modelling Library (Valente, 1998) is an example of such a library, as is Ontoligua server (Farquhar et al 1996). One possible future development is the setting up of a library of spatial ontologies.…”

Section: Commonkads Librarymentioning

confidence: 99%

“…Given that CommonKADS is designed to exploit ontologies in the form of the CommonKADS Library (Valente et al, 1998), collaborative systems could be built reusing knowledge from the domain layer and inference layer of the knowledge model. In other words there is no need to build specific spatial extensions to CommonKADS, rather a library of model components of a spatial nature could be developed.…”

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confidence: 99%

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Developing Smart Spatial Systems Using CommonKADS

Crowther¹,

Hartnett²

2004

GeoDynamics

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The aim of this paper is to show how spatial reasoning systems in a geographic context can be developed using CommonKADS. KADS was initiated as a "Structured methodology for the development of knowledge based systems" (Motta. 1997). The limitations of production rules, combined with their inherent non-reusability contributed significantly to the impetus to develop methodologies like KADS. The two central principles that underlie the KADS approach are the introduction of multiple models as a means of coping with the complexity of the knowledge engineering process, and the use of knowledge-level descriptions as an intermediate model between expertise data and system design.Motta (1997) coins the term "knowledge modelling revolution", which refers to the paradigm switch from symbol level (rule based) approaches to knowledge level task centred analysis.This heralded the necessary decoupling of the task specification and the problem solving method.The CommonKADS methodology has been used extensively in a wide range of domains, but not that of spatial reasoning. The advantage of CommonKADS is that it provides a full model of an application, not just a model of the knowledgebase (Schriber et al 2000) .Visual geographic knowledge which can be extracted from satellite remote sensing images has characteristics which are not commonly found in nonvisual domains. Traditionally geographic expert systems have worked either at the pixel level of raster images or the object level of vector images. This has shortfalls when knowledge acquired from a human image interpreter has to be incorporated into an expert system to aid interpretation.

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Section: Figure 3 Composite Image Of Part Of the Study Area Around Dementioning

confidence: 99%

Section: Commonkads Librarymentioning

confidence: 99%

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confidence: 99%

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Developing Smart Spatial Systems Using CommonKADS

Crowther¹,

Hartnett²

2004

GeoDynamics

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“…In CommonKADS methodology and related work [Schreiber et al 1994, Valente et al 1998, method capabilities are represented in a functional way, i.e., by specifying inputs and outputs, plus the knowledge used in the process (called static knowledge). Part of the semantics was also expressed by relating the method to an element of a typology of methods, typically at the lowest grain size level (the so-called canonical inferences, see [Aben, 1993]) or at the highest grain-size level (e.g.…”

Section: Related Workmentioning

confidence: 99%

Representing Capabilities of Problem Solving Methods

Swartout¹,

Gil²,

Valente³

1999

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In order to develop and use shared libraries of problem-solving methods, it is of paramount importance to provide adequate descriptions of their capabilities and competence. Methods must be indexed and organized based on their capabilities so that they can be retrieved when their capability is adequate for the task at hand. This paper describes the approach taken in EXPECT for representing method capabilities and argues that it has important features that should be used for describing methods in shared libraries.EXPECT's capability representation is tightly coupled with the domain ontologies in the knowledge base, can express task-related parameters explicitly, and is based on case grammars. This representation allows the system to reason about the capability descriptions through class subsumption and reformulation. The benefits of this approach include self-organizing method libraries, reuse, and support for explanation. The representation has already been used extensively within EXPECT to express a wide range of method capabilities, ranging from abstract to specific, small to large, and domain-dependent to general-purpose methods.The paper also discusses some of the additional features that we anticipate will be useful to structure shared method libraries.

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“…In KADS there is an attempt to offer some guidance in that there is a library of tasks, and it is the choice of tasks that mainly determines the form of the ontology. But even so this guidance remains somewhat abstract, because of the limited granularity of the library components (Valente and Lockenhoff, 1994). In our view there is a need for an analytic stage prior to embarking on the KADS method, so as to provide a broader and richer characterisation of the domain, which will provide a context within which questions such as what the form of the ontology will be can be answered.…”

Section: Introductionmentioning

confidence: 99%