Structure-preserving knowledge-based system development through reusable libraries: a case study in diagnosis

Benjamins, V. Richard; Aben, Manfred

doi:10.1006/ijhc.1997.0117

Cited by 15 publications

(20 citation statements)

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“…Implemented libraries provide operational specifications of PSMs, which are directly executable ( [123], [71]). Formal libraries allow for formal verification of properties of PSMs ( [2], [3], [15], [139]). Finally, informal libraries provide structured textual representations of PSMs.…”

Section: Types Of Psm Librariesmentioning

confidence: 99%

Knowledge engineering: Principles and methods

Studer

Benjamins

Fensel

1998

Data & Knowledge Engineering

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2,735

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: Types Of Psm Librariesmentioning

confidence: 99%

Knowledge engineering: Principles and methods

Studer

Benjamins

Fensel

1998

Data & Knowledge Engineering

Self Cite

2,735

1,008

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“…process-specific representation and reasoning languages, e.g., PSL [5] and SPARK-L [23]; 3. Semantic Web service ontologies, e.g., WSMO 4 and OWL-S 5 ; and 4. process specification and execution languages, e.g., BPEL and XPDL. 6 However, while individually each of these approaches is expressive in terms of workflow constructions and reasoning capabilities, SMEs still require further support to effectively model processes, as shown in [31] and [15].…”

Section: Requirementsmentioning

confidence: 99%

“…A large amount of work in knowledge-based systems in the past three decades, like [11], [4], and [27], has concentrated on providing frameworks and tools that support the collaboration of KEs and SMEs with the goal of alleviating the knowledge acquisition bottleneck. However, despite progress shown by existing knowledge acquisition tools like KRAKEN [25], SHAKEN [3] and, more recently, AURA [10] and the Halo extension of the Semantic MediaWiki [18], it is still a complex problem to enable SMEs to capture the knowledge from a domain by themselves, especially for some knowledge types.…”

Section: Introductionmentioning

confidence: 99%

A Formalism and Method for Representing and Reasoning with Process Models Authored by Subject Matter Experts

Gómez-Pérez¹,

Erdmann²,

Greaves

et al. 2013

IEEE Trans. Knowl. Data Eng.

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Abstract-Enabling Subject Matter Experts (SMEs) to formulate knowledge without the intervention of Knowledge Engineers (KEs) requires providing SMEs with methods and tools that abstract the underlying knowledge representation and allow them to focus on modeling activities. Bridging the gap between SME-authored models and their representation is challenging, especially in the case of complex knowledge types like processes, where aspects like frame management, data, and control flow need to be addressed. In this paper, we describe how SME-authored process models can be provided with an operational semantics and grounded in a knowledge representation language like F-logic to support process-related reasoning. The main results of this work include a formalism for process representation and a mechanism for automatically translating process diagrams into executable code following such formalism. From all the process models authored by SMEs during evaluation 82 percent were well formed, all of which executed correctly. Additionally, the two optimizations applied to the code generation mechanism produced a performance improvement at reasoning time of 25 and 30 percent with respect to the base case, respectively. 1INTRODUCTIONB UILDING knowledge-based systems is an activity that has been traditionally carried out by a combination of software and knowledge engineers (KEs) and of subject matter experts (SMEs), also known as domain experts. Software engineers (SEs) are focused on architectural and user interface issues related to the development of software. KEs are focused on knowledge acquisition and representation tasks, with the aim of building the required knowledge bases. For these tasks, KEs usually work in collaboration with SMEs, who normally act as repositories of domain knowledge. The combination of KEs and SMEs, although feasible, has two main drawbacks, first characterized as the knowledge acquisition bottleneck [13]: 1) it is costly and 2) it can be error prone, especially in complex domains.A large amount of work in knowledge-based systems in the past three decades, like [11] Among the different types of knowledge that can be used in knowledge-based systems, e.g., factual, rule or causal knowledge, we focus on process knowledge, which is widely used across domains while posing important challenges for knowledge acquisition. A process can be defined as a naturally occurring or designed sequence of changes of properties of a system or object. Examples of processes include the replication of DNA, the mitosis of the cell or a combustion reaction. Processes encapsulate such things as preconditions and postconditions, results, contents, actors or causes and relate to the sequence of operations and involved events taking up time, space, expertise or other resources, which lead to the production of an outcome. For example, consider the case of a complex chemical reaction comprising several steps, with different inputs and outputs, where it is necessary to reason about what would happen at a certain stage if a previou...

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“…Legenda: premise and/or conclusion inference these assumptions ( [Benjamins & Aben, 1997], [Fensel, 1995a], [Fensel et al, 1996], [Wielinga et al, 1995], [Benjamins & Pierret-Golbreich, 1996], [Benjamins et al, 1996], , [Fensel & Straatman, to appear], [O´Hara & Shadbolt, 1996], , [Breuker, 1997], [Fensel & Schönegge, submitted]).…”

Section: Introductionmentioning

confidence: 99%

“…Also, we sketch the twofold role assumptions can play and express the relationship between these two roles as the law of conservation of assumptions (cf. [Benjamins et al, 1996]). In Section 3, we provide an extensive survey on assumptions used in diagnostic problem solving.…”

Section: Introductionmentioning

confidence: 99%

The role of assumptions in knowledge engineering

Fensel

Benjamins

1998

Int. J. Intell. Syst.

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Problem-solving methods are means to describe the inference process of knowledge-based systems. During the last years, a number of these problemsolving methods have been identified that can be reused for building new systems. However, problem-solving methods require specific types of domain knowledge and introduce specific restrictions on the tasks that can be solved by them. These requirements and restrictions are assumptions that play a key role in reusing problem-solving methods, in acquiring domain knowledge, and in defining the problem that can be tackled by the knowledge-based systems. In the paper, we discuss the different roles, assumptions play in the development process of knowledge-based systems and provide a survey of assumptions used by diagnostic problem solving. We show how such assumptions introduce target and bias for goal-driven machine learning and knowledge discovery techniques. 1. Exceptions are classification problems which have often known polynomial time complexity (see [Goel et al., 1987]).

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Structure-preserving knowledge-based system development through reusable libraries: a case study in diagnosis

Cited by 15 publications

References 20 publications

Knowledge engineering: Principles and methods

Knowledge engineering: Principles and methods

A Formalism and Method for Representing and Reasoning with Process Models Authored by Subject Matter Experts

The role of assumptions in knowledge engineering

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