Knowledge Engineering: Survey and Future Directions

Studer, Rudi; Fensel, Dieter; Decker, Stefan; Benjamins, V. Richard

doi:10.1007/10703016_1

Cited by 18 publications

(10 citation statements)

References 48 publications

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“…Climate mapping situations are ideal candidates for the application of a KBS approach; data are unrepresentative and new and unexpected situations continually arise, but a large body of expert knowledge is available. KBS technology is a companion to expert systems, one of the most successful branches of artificial intelligence research (Doukidis & Whitley 1988, Studer et al 1999. KBS has been applied in the fields of computer science, natural resource management (Schmoldt & Rauscher 1996), hydrologic modeling (Lam & Swayne 1993), meteorology (Jones & Roydhouse 1994), and GIS (geographical information systems; Yuan 1997).…”

Section: Introductionmentioning

confidence: 99%

A knowledge-based approach to the statistical mapping of climate

Daly

Gibson²,

Taylor³

et al. 2002

Clim. Res.

993

797

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The demand for spatial climate data in digital form has risen dramatically in recent years. In response to this need, a variety of statistical techniques have been used to facilitate the production of GIS-compatible climate maps. However, observational data are often too sparse and unrepresentative to directly support the creation of high-quality climate maps and data sets that truly represent the current state of knowledge. An effective approach is to use the wealth of expert knowledge on the spatial patterns of climate and their relationships with geographic features, termed 'geospatial climatology', to help enhance, control, and parameterize a statistical technique. Described here is a dynamic knowledge-based framework that allows for the effective accumulation, application, and refinement of climatic knowledge, as expressed in a statistical regression model known as PRISM (parameter-elevation regressions on independent slopes model). The ultimate goal is to develop an expert system capable of reproducing the process a knowledgeable climatologist would use to create high-quality climate maps, with the added benefits of consistency and repeatability. However, knowledge must first be accumulated and evaluated through an ongoing process of model application; development of knowledge prototypes, parameters and parameter settings; testing; evaluation; and modification. This paper describes the current state of a knowledge-based framework for climate mapping and presents specific algorithms from PRISM to demonstrate how this framework is applied and refined to accommodate difficult climate mapping situations. A weighted climate-elevation regression function acknowledges the dominant influence of elevation on climate. Climate stations are assigned weights that account for other climatically important factors besides elevation. Aspect and topographic exposure, which affect climate at a variety of scales, from hill slope to windward and leeward sides of mountain ranges, are simulated by dividing the terrain into topographic facets. A coastal proximity measure is used to account for sharp climatic gradients near coastlines. A 2-layer model structure divides the atmosphere into a lower boundary layer and an upper free atmosphere layer, allowing the simulation of temperature inversions, as well as mid-slope precipitation maxima. The effectiveness of various terrain configurations at producing orographic precipitation enhancement is also estimated. Climate mapping examples are presented.KEY WORDS: Climate map · Knowledge-based system · Climate interpolation · Spatial climate · Climate data sets · GIS · PRISM · Geospatial climatology Resale or republication not permitted without written consent of the publisherClim Res 22: [99][100][101][102][103][104][105][106][107][108][109][110][111][112][113] 2002 expert and statistical. Human-expert methods use human experience, expertise, and knowledge acquisition capabilities to infer climate patterns from meteorological regimes, physiographic features, biotic character...

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Section: Introductionmentioning

confidence: 99%

A knowledge-based approach to the statistical mapping of climate

Daly

Gibson²,

Taylor³

et al. 2002

Clim. Res.

993

797

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“…While these technologies are very valuable from a developer's point of view, they wouldn't condition domain experts unnecessarily during the characterization process of the LO in their domains of expertise. On the contrary, this activity could be better based on techniques used in software engineering [1] and knowledge engineering [11] for the construction of domain models.…”

Section: Products and Activitiesmentioning

confidence: 99%

Building repositories of learning objects in specialized domains: the Chasqui approach

Sierra

Fernández‐Valmayor

Guinea

et al. 2005

Fifth IEEE International Conference on Advanced Learning Technologies (ICALT'05)

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In this paper we describe the Chasqui approach to the construction of repositories of learning objects (LO) in specific knowledge areas. This approach is the result of our experiences in the virtualization of academic museums at the Complutense University of Madrid (Spain) as well as of some experiences in the virtualization of the campus of this university. This approach promotes a close collaboration between two kinds of actors: domain experts (e.g. researchers and lecturers) and developers. This collaboration results in (i) the definition of a suitable model for the LO in the domain of interest, (ii) the construction by developers of a domain-specific tool for authoring LO conforming this model, and (iii) the population of the repository by domain experts using this tool. The domain specific LO models and the domain specific tools based on these models facilitate the production and maintenance of the repositories and the exploitation of the educational potential of the resources used by the experts in their daily work.

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“…The notion of ontologies is fundamental in this context. An ontology [24] is an explicit specification of concepts and the relationships among them. Ontologies can be either represented by a network of interrelated terms, or -as demonstrated in [14] -as a hierarchy of object oriented classes and instances.…”

Section: 22mentioning

confidence: 99%

Capture and Dissemination of Experience about the Construction of Engineering Processes

Rupprecht¹,

Fünffinger²,

Knublauch³

et al. 2000

Notes on Numerical Fluid Mechanics and Multidisciplinary Design

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Process know-how is instrumental to govern engineering processes in a network of engineering departments as well as migrate changes of processes due to emerging technological or other opportunities. In this paper we present a process construction kit for the capture and dissemination of process knowledge at the level of process structures as well as process construction experience. Our approach ranges from the formal capture and maintenance of know-how about processes to the implementation of process adaptations in terms of objectoriented concepts. Requirements are drawn from specific reference applications in the automotive engineering and the plant construction domain.

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Knowledge Engineering: Survey and Future Directions

Cited by 18 publications

References 48 publications

A knowledge-based approach to the statistical mapping of climate

A knowledge-based approach to the statistical mapping of climate

Building repositories of learning objects in specialized domains: the Chasqui approach

Capture and Dissemination of Experience about the Construction of Engineering Processes

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