Knowledge Acquisition for Expert Systems

Hart, Anna

doi:10.1007/978-1-4471-1632-5_10

Cited by 195 publications

(106 citation statements)

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“…Whilst there arc a growing number of articles and books available on 'how to do knowledge elicitation', these often contain advice of the most general kind, and emphasise the pragmatic considerations of expert system development (cf for example. Wclbank, 1983;Hart, 1986).…”

Section: Section 2 Knowledge Acquisition: Methodologies Models and Fmentioning

confidence: 99%

Contemporary Knowledge Engineering and Cognition

Schmalhofer¹,

Strube²,

Wetter³

1992

Lecture Notes in Computer Science

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Section: Section 2 Knowledge Acquisition: Methodologies Models and Fmentioning

confidence: 99%

Contemporary Knowledge Engineering and Cognition

Schmalhofer¹,

Strube²,

Wetter³

1992

Lecture Notes in Computer Science

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“…Conventional system develop ment approaches will involve 1) Processing of data and information, 2) Algorith ms 3) Control Sequence 4) System understanding 5) Expertise of the developing team in the technology [4,5]. However in designing and developing an expert system in a domain, and making it work as effective or more effect ive than human, is to first understand the design requirement of such system, how it should work, and the deliverables.…”

Section: B Ku: Building Blockmentioning

confidence: 99%

A Review on the Knowledge Representation Models and its Implications

V¹,

Selwyn²

2016

IJITCS

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Abstract-Data and Informat ion has seen exponential growth in the past few years which has led to its importance in processing it in the creation of knowledge. Representing knowledge in a required format is the need for the building a knowledgebase (KB) for Expert Systems. In this paper we carried a survey on the knowledge representation models that will help us choose a suitable model for designing and developing a KB. A detailed study is conducted on six models and comparison of the models on some non-functional attributes are carried out to enable knowledge workers to decide on the model selection.

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“…Open-ended issues such as personalities, manners of expression, reasoning processes of the experts, forms of questions and follow-ups, recording and notation methods, must be addressed in order to transcribe accurately what the experts mean. We designed our knowledge acquisition sessions based on the works of (Scott, Clayton, and Gibson 1991) and (Hart 1992): protocol, blind test, and cross reference. We conducted interviews with three expert sea ice interpreters, two from the Canadian Ice Service (CIS) and one from the National Ice Center (NIC).…”

Section: Knowledge Acquisitionmentioning

confidence: 99%

“…The blind test method is based on the twenty questions concept (Hart 1992), where the interviewed expert asks questions about an unseen case, and it forces the expert to verbally describe the classification process by classifying an image without actually seeing it. In this sense, we were able to obtain the chain of reasoning-observations to conclusions-explicitly from the experts.…”

Section: Knowledge Acquisitionmentioning

confidence: 99%

ARKTOS: a knowledge engineering software tool for images

Soh¹,

Tsatsoulis²

2002

International Journal of Human-Computer Studies

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-The goal of our ARKTOS project is to build an intelligent knowledge-based system to classify satellite sea ice images. It involves acquiring knowledge from sea ice experts, quantifying such knowledge as computational entities, and ultimately building an intelligent classifier. In this paper we describe a two-stage knowledge engineering approach that facilitates explicit knowledge transfer, converting implicit visual cues and cognition of the experts to explicit attributes and rules implemented by the engineers. First, there is a prototyping stage that involves interviewing sea ice experts, transcribing the sessions, identifying descriptors and rules, designing and implementing the knowledge, and delivering the prototype. The objective of this stage is to obtain a modestly accurate classification system quickly. Second, there is a refinement stage that involves evaluating the prototype, refining the knowledge base, modifying the design, and re-evaluating the improved system. Since the refinement is evaluation-driven, the experts and the engineers are motivated explicitly to improve the knowledge base and are able to communicate with each other using a common, consistent platform. Moreover, since the classification result is immediately available, both sides are able to efficiently assess the correctness of the system. To facilitate the knowledge engineering of the second stage, we have designed and built three Java-based graphical user interfaces: arktosGUI, arktosViewer, and arktosEditor. arktosGUI concentrates on feature-based refinement of specific attributes and rules. arktosViewer deals with regional evaluation. arktosEditor has a rule indexing and search mechanism and knowledge base editing capabilites. INTRODUCTIONThe goal of our ARKTOS (Advanced Reasoning using Knowledge for Typing Of Sea ice) project is to perform automated, intelligent satellite sea ice image classification. Our approach is to acquire the classification knowledge from sea ice geophysicists and photo-interpreters and implement the knowledge in a rule-based system that also utilizes image processing methodologies.To facilitate a knowledge transfer from the sea ice experts to the knowledge and software engineers, we employ a two-stage knowledge engineering approach that includes rapid prototyping and evaluation-driven refinement. The rapid prototyping allows the experts and the engineers to design and implement a modestly accurate classification system. Given the functional system, the experts are then able to evaluate its classification results. To facilitate the evaluation process, we create Java-based software tools such that implicit visual cues and cognition of the experts can be explicitly expressed and fine-tuned in attributes and rules. The prototyping stage involves

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Knowledge Acquisition for Expert Systems

Cited by 195 publications

References 0 publications

Contemporary Knowledge Engineering and Cognition

Contemporary Knowledge Engineering and Cognition

A Review on the Knowledge Representation Models and its Implications

ARKTOS: a knowledge engineering software tool for images

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