(ML)2: A formal language for KADS models of expertise

Harmelen, Frank van; Balder, John

doi:10.1016/1042-8143(92)90017-u

Cited by 78 publications

(32 citation statements)

References 8 publications

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“…Two types of languages are offered to describe an Expertise Model: CML (Conceptual Modeling Language) [127], which is a semi-formal language with a graphical notation, and (ML) 2 [79], which is a formal specification language based on first order predicate logic, meta-logic and dynamic logic (see Section 4). Whereas CML is oriented towards providing a communication basis between the knowledge engineer and the domain expert, (ML) 2 is oriented towards formalizing the Expertise Model.…”

Section: The Commonkads Approachmentioning

confidence: 99%

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Knowledge engineering: Principles and methods

Studer

Benjamins

Fensel

1998

Data & Knowledge Engineering

2,671

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: The Commonkads Approachmentioning

confidence: 99%

“…[126]), and, on the other hand, they correspond e.g. to the lift operator in (ML) 2 [79] or the view definitions in KARL [53] (compare Section 4). PROTÉGÉ-II aims at providing a small collection of rather simple mappings to limit the reuse effort needed to specify these mappings.…”

Section: The Protégé-ii Approachmentioning

confidence: 99%

Knowledge engineering: Principles and methods

Studer

Benjamins

Fensel

1998

Data & Knowledge Engineering

2,671

1,008

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“…(ML) 2 has been applied in a dozen formalizations of models of expertise [HaB92], e.g., for making predictions about physical systems by means of qualitative reasoning [BRW90]. The problem-solving methods Cover and Differentiate and Heuristic Classification [SWA92] are formalized in (ML) 2 .…”

Section: Applications and Discussionmentioning

confidence: 99%

A comparison of languages which operationalize and formalize KADS models of expertise

Fensel¹,

Harmelen

1994

The Knowledge Engineering Review

Self Cite

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Abstract.In the field of Knowledge Engineering, dissatisfaction with the rapid-prototyping approach has led to a number of more principled methodologies for the construction of knowledgebased systems. Instead of immediately implementing the gathered and interpreted knowledge in a given implementation formalism according to the rapid-prototyping approach, many such methodologies centre around the notion of a conceptual model: an abstract, implementation independent description of the relevant problem solving expertise. A conceptual model should describe the task which is solved by the system and the knowledge which is required by it. Although such conceptual models have often been formulated in an informal way, recent years have seen the advent of formal and operational languages to describe such conceptual models more precisely, and operationally as a means for model evaluation. In this paper, we study a number of such formal and operational languages for specifying conceptual models. In order to enable a meaningful comparison of such languages, we focus on languages which are all aimed at the same underlying conceptual model, namely that from the KADS method for building KBS. We describe eight formal languages for KADS models of expertise, and compare these languages with respect to their modelling primitives, their semantics, their implementations and their applications. Future research issues in the area of formal and operational specification languages for KBS are identified as the result of studying these languages. The paper also contains an extensive bibliography of research in this area.

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“…Als Nachteil der modellbasierten Wissensakquisition ergeben sich Pro-bleme mit einer frühen Validierung durch die Benutzer, da die (natürlichsprachliche) Spezifikation von diesen oft nicht hinreichend verstanden wird. Verschiedene Autoren ( [1], [26], [27], [25]) haben aus diesem Grund formale, operationale Spezifikationssprachen für "Models of Expertise" entwickelt. Unser Ansatz geht einen anderen Weg: Wir entwickeln einen Interpreter, der eine semi-formale Spezifikation in Interaktion mit dem Benutzer abarbeiten kann und dabei auch das "Model of Cooperation" validiert.…”

Section: Rapid Prototyping Unterstützt Die Validierung Von Systemen Uunclassified

Validierung von konzeptuellen Modellen

Maurer

Pews

1993

Expertensysteme 93

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(ML)2: A formal language for KADS models of expertise

Cited by 78 publications

References 8 publications

Knowledge engineering: Principles and methods

Knowledge engineering: Principles and methods

A comparison of languages which operationalize and formalize KADS models of expertise

Validierung von konzeptuellen Modellen

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