Towards method-independent knowledge acquisition

Gil, Yolanda; Paris, Cécile

doi:10.1006/knac.1994.1009

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…It should be clear that there exist further approaches which are well known in the KE community, like e.g VITAL [130], Commet [136], and EXPECT [72]. However, a discussion of all these approaches is beyond the scope of this paper.…”

Section: Modeling Frameworkmentioning

confidence: 99%

Knowledge engineering: Principles and methods

Studer

Benjamins

Fensel

1998

Data & Knowledge Engineering

2,680

1,008

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This paper gives an overview about the development of the field of Knowledge Engineering over the last 15 years. We discuss the paradigm shift from a transfer view to a modeling view and describe two approaches which considerably shaped research in Knowledge Engineering: Role-limiting Methods and Generic Tasks. To illustrate various concepts and methods which evolved in the last years we describe three modeling frameworks: CommonKADS, MIKE, and PROTÉGÉ-II. This description is supplemented by discussing some important methodological developments in more detail: specification languages for knowledge-based systems, problem-solving methods, and ontologies. We conclude with outlining the relationship of Knowledge Engineering to Software Engineering, Information Integration and Knowledge Management. Key WordsKnowledge Engineering, Knowledge Acquisition, Problem-Solving Method, Ontology, Information Integration IntroductionIn earlier days research in Artificial Intelligence (AI) was focused on the development of 2 formalisms, inference mechanisms and tools to operationalize Knowledge-based Systems (KBS). Typically, the development efforts were restricted to the realization of small KBSs in order to study the feasibility of the different approaches.Though these studies offered rather promising results, the transfer of this technology into commercial use in order to build large KBSs failed in many cases. The situation was directly comparable to a similar situation in the construction of traditional software systems, called "software crisis" in the late sixties: the means to develop small academic prototypes did not scale up to the design and maintenance of large, long living commercial systems. In the same way as the software crisis resulted in the establishment of the discipline Software Engineering the unsatisfactory situation in constructing KBSs made clear the need for more methodological approaches.So the goal of the new discipline Knowledge Engineering (KE) is similar to that of Software Engineering: turning the process of constructing KBSs from an art into an engineering discipline. This requires the analysis of the building and maintenance process itself and the development of appropriate methods, languages, and tools specialized for developing KBSs.Subsequently, we will first give an overview of some important historical developments in KE: special emphasis will be put on the paradigm shift from the so-called transfer approach to the so-called modeling approach. This paradigm shift is sometimes also considered as the transfer from first generation expert systems to second generation expert systems [43]. Based on this discussion Section 2 will be concluded by describing two prominent developments in the late eighties: Role-limiting Methods [99] and Generic Tasks [36]. In Section 3 we will present some modeling frameworks which have been developed in recent years:, and PROTÈGÈ-II [123]. Section 4 gives a short overview of specification languages for KBSs. Problem-solving methods have been a major research ...

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Section: Modeling Frameworkmentioning

confidence: 99%

Knowledge engineering: Principles and methods

Studer

Benjamins

Fensel

1998

Data & Knowledge Engineering

2,680

1,008

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“…The idea in EXPECT [Swartout and Gil 1995, Gil 1994, Gil and Paris 1994 is to derive the interdependencies between domain knowledge and problem solving methods automatically, and to be able to re-derive the dependencies as needed when changes are made to the problem solving knowledge. This approach allows a user to modify either the domain knowledge or the problem solving knowledge.…”

Section: Flexibility Through Derived Interdependenciesmentioning

confidence: 99%

“…We will then describe EXPECT, the acquisition framework we have been developing [Swartout and Gil 1995, Gil 1994, Gil and Paris 1994. Each of these four systems represents a point along the trend we outlined above.…”

Section: Introductionmentioning

confidence: 99%

Flexible Knowledge Acquisition Through Explicit Representation of Knowledge Roles

Swartout¹,

Gil²

1996

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A system that acquires knowledge from a user should be able to reflect upon the knowledge that it has-at each moment-and understand what kinds of new knowledge it needs to learn. For the past two decades, research in the area of knowledge acquisition has been moving towards systems that have access to richer representations of knowledge about their task. This paper reviews some well-known knowledge acquisition tools representative of this trend. It also describes our recent work in EXPECT, a system with explicit representations of knowledge about the task and the domain that supports knowledge acquisition for a wider range of tasks and applications than its predecessors. We hope our observations will be useful to researchers in user interfaces and in machine learning concerned with acquiring information from users.

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