The Knowledge Acquisition and Representation Language, KARL

Fensel, Dieter; Angele, Jürgen; Studer, Rudi

doi:10.1007/978-1-4615-2275-1

Cited by 50 publications

(18 citation statements)

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“…KARL is a customization of first-order logic consisting of the two sublanguages L-KARL that represents static knowledge and P-KARL that represents dynamic knowledge. It has a declarative semantics [Fen93a] as well as an operational semantics [Ang93]. L-KARL is based on Frame-logic [KLW93], which integrates objectorientation into a declarative framework.…”

Section: How To Bridge the Representation Gapmentioning

confidence: 99%

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A comparison of two approaches to model-based knowledge acquisition

Fensel¹,

Poeck

1994

Lecture Notes in Computer Science

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Abstract. This paper discusses and compares two different approaches to model-based knowledge acquisition. That is, we regard the Model-based and Incremental Knowledge Engineering (MIKE) approach and the Configurable Role-limiting Method approach (CRLM). MIKE is based on the distinction of different phases in the software development process. It uses the formal and operational knowledge specification language KARL allowing a precise and unique description of a model of expertise which is the outcome of the analysis phase. CRLM is based on the rolelimiting method approach. Role limiting shells are implementations of strong problem-solving methods and substantially simplify knowledge acquisition through guidance by predefined models of problem-solving and by sophisticated graphical user interfaces. The main disadvantages, namely inflexibility and brittleness, are to some degree overcome by the CRLM where the shell's problem-solving methods are split into smaller parts, which can then be reconfigured allowing the integration of new methods or other method combinations. Although these two approaches are often discussed as contradictory, we, however, experienced that both approaches complete each other very well. As an outcome of our comparison, we outline topics of future research for both approaches.

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Section: How To Bridge the Representation Gapmentioning

confidence: 99%

“…An important means of MIKE is the formal and operational knowledge specification language KARL (cf. [FAL91], [Fen93a], [AFS94]), which allows a precise and unequivocal description of a model of expertise as the result of the analysis phase. [PoG93] is based on the role-limiting method approach (see [Mar88], [McD88]).…”

Section: Introductionmentioning

confidence: 99%

A comparison of two approaches to model-based knowledge acquisition

Fensel¹,

Poeck

1994

Lecture Notes in Computer Science

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“…[14], [15]) which has been proved useful in the specification of KBSs (cf. KARL [7], (ML) 2 [31], and MLPM [9]). Dynamic logic has two main advantages (especially if compared to first-order predicate logic).…”

Section: Competencementioning

confidence: 99%

“…we have verified with KIV theorems of [3] used to distinguish different complexity classes in abduction. 7 Notice that these proofs are in no way trivial: the informal proof for theorem 5.3 of [3] took one page. It states that for the class of ordered monotonic abduction problems using a specific preference criterion, there is a polynomial algorithm for finding a best explanation.…”

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confidence: 99%

Using KIV to specify and verify architectures of knowledge-based systems

Fensel¹,

Schnogge²

Proceedings 12th IEEE International Conference Automated Software Engineering

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examples. E.g. we have verified with KIV theorems of [3] used to distinguish different complexity classes in abduction. 7 Notice that these proofs are in no way trivial: the informal proof for theorem 5.3 of [3] took one page. It states that for the class of ordered monotonic abduction problems using a specific preference criterion, there is a polynomial algorithm for finding a best explanation. This is proved by presenting an algorithm that in polynomial time returns such a best explanation. Formalizing the informal arguments of the correctness proof for this algorithm results in several hundred (machine checkable) proof steps.Roughly, the interactive theorem proving system of KIV is comparable with systems as PVS [5] and Isabelle [22]. For our purpose, the KIV system is especially well suited due to its facilities for structuring specifications and software modules (including automatic generation of proof obligations), its proof engineering facilities (like an elaborated graphical user interface and reuse mechanisms), and the underlying dynamic logic.[20] identifies two kinds of approaches in software reuse: Supporting the software development process with reusable components or making parts of the development process reusable via program transformation techniques. Our approach provide support by formally specified and verified building blocks i.e. components. The latter approach is taken by KIDS/SPECWARE [28], [29] which provides support in the derivation of efficient implementations from formal specifications. Here, problem-solving methods are not "first-order citizens" that describe reusable components or architectures but secondorder transformation rules working on specifications. As in our approach, system development is viewed as a semiautomatic activity. At the technical level, the main differences are the use of dynamic logic for the declarative specification of procedural constructs in KIV and the use of category theory and sheaf theory to express transformations of algebraic specifications in SPECWARE.AMPHION ([18], [19]) is a knowledge-based software engineering system for the formal specification and automatic deductive synthesis of programs which consist of calls of subroutines from a library. It is specialized to application domains by means of a declarative domain theory and a library of subroutines. This specialization allows the automatic synthesis of programs from specifications. Our approach is more general-purpose (but, of course, less automatic): the programs developed are combinations and instantiations of (mostly domain-7. Theorem 4.4 and (the more difficult) theorem 5.3. For both theorems only the total correctness of the algorithms and, of course, not their complexity bounds have been proven. independent) problem-solving methods rather then simply a sequence of calls of subroutines from a library. Furthermore the (normal) user of the AMPHION system is not intended to create or modify the domain theory or the subroutine library. In our approach, the verification of user-defined problem-...

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“…The main restriction for executable specifications is that the perfect Herbrand models have to be finite. For more details see [Fen93] and [Ang93].…”

Section: The Formal and Executable Levelmentioning

confidence: 99%

Graphical and formal knowledge specification with KARL

Fensel

Proceedings of International Conference on Expert Systems for Development

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: The paper discusses an approach which allows the specification of a knowledge-based system (kbs) at several levels. The Knowledge Acquisition and Representation Language KARL combines a description of a kbs at the conceptual level supported by graphical modelling primitives with a description at a formal and executable level. Therefore, a KARLspecification can be used as a means for communication between expert and knowledge engineer as well as an intermediate representation, closing the conceptual gap between an informal specification and an implementation of a kbs. In the paper, KARL is mainly discussed as a graphical modelling language .

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The Knowledge Acquisition and Representation Language, KARL

Cited by 50 publications

References 55 publications

A comparison of two approaches to model-based knowledge acquisition

A comparison of two approaches to model-based knowledge acquisition

Using KIV to specify and verify architectures of knowledge-based systems

Graphical and formal knowledge specification with KARL

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