Flexible Knowledge Acquisition Through Explicit Representation of Knowledge Roles

Swartout, Bill; Gil, Yolanda

doi:10.21236/ada459767

Cited by 18 publications

(18 citation statements)

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“…Early ontology engineering methodologies such as Uschold and King (1995), Swartout et al . (1996), and Fox and Gruninger (1998) (see Fernández-López & Gómez-Pérez, 2002 for an overview) focussed on core ontology development activities: requirements analysis, conceptualization, implementation, evaluation and maintenance. They assume that the formal specification of the domain knowledge to be used in an application system precedes the actual development of the system.…”

Section: Ontology Engineering Methodologiesmentioning

confidence: 99%

Collaborative ontology engineering: a survey

Simperl

Luczak-Rösch

2013

The Knowledge Engineering Review

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Building ontologies in a collaborative and increasingly community-driven fashion has become a central paradigm of modern ontology engineering. This understanding of ontologies and ontology engineering processes is the result of intensive theoretical and empirical research within the Semantic Web community, supported by technology developments such as Web 2.0. Over 6 years after the publication of the first methodology for collaborative ontology engineering, it is generally acknowledged that, in order to be useful, but also economically feasible, ontologies should be developed and maintained in a community-driven manner, with the help of fully-fledged environments providing dedicated support for collaboration and user participation. Wikis, and similar communication and collaboration platforms enabling ontology stakeholders to exchange ideas and discuss modeling decisions are probably the most important technological components of such environments. In addition, process-driven methodologies assist the ontology engineering team throughout the ontology life cycle, and provide empirically grounded best practices and guidelines for optimizing ontology development results in real-world projects. The goal of this article is to analyze the state of the art in the field of collaborative ontology engineering. We will survey several of the most outstanding methodologies, methods and techniques that have emerged in the last years, and present the most popular development environments, which can be utilized to carry out, or facilitate specific activities within the methodologies. A discussion of the open issues identified concludes the survey and provides a roadmap for future research and development in this lively and promising field.

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Section: Ontology Engineering Methodologiesmentioning

confidence: 99%

Collaborative ontology engineering: a survey

Simperl

Luczak-Rösch

2013

The Knowledge Engineering Review

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“…From a knowledge acquisition (KA) perspective, RCS is not formal, like EXPECT [11], CommonKADS [12], and other methods and tools. These other approaches focus more on the knowledge in the system by knowledge engineers, rather than the construction of an executable system that is accessible to the domain expert.…”

Section: Representation and Methodology Comparisonmentioning

confidence: 99%

How task analysis can be used to derive and organize the knowledge for the control of autonomous vehicles

Barbera

Albus

Messina

et al. 2004

Robotics and Autonomous Systems

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“…KL-B is the major focus of much of the KA community, e.g. generic tasks (Chandrasekaran et al, 1992); con®gurable role-limiting methods (Swartout and Gill, 1996;Gil and Melz, 1996); SPARK/BURN/FIREFIGHTERÐhereafter, SBF (Marques et al, 1992); model construction operators (Clancey, 1992); CommonKADS (Wielinga et al, 1992;Schreiber et al, 1994); the Method To-Task approach (Eriksson et al, 1995); components of expertise (Steels, 1990); MIKE (Angele et al, 1996). Libraries of PSMs are described in Benjamins (1995), Breuker and de Velde (1994), Chandrasekanan et al (1992), Motta and Zdrahal (1996) Tansley and Hayball (1993).…”

Section: Psms: Problem Solving Methodsmentioning

confidence: 99%

Knowledge maintenance: the state of the art

Menzies

1999

Knowl. Eng. Rev.

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The software and knowledge engineering literature de nes maintenance strategies for seven main types of knowledge: words; sentences; behavioural knowledge; and meta-knowledge. Meta-knowledge divides into problem solving methods; quality knowledge; x knowledge; social knowl-5 edge; and processing activities. There are ve main ways in which these seven knowledge types are processed: acquire; operationalise; fault; x; and preserve. We review systems that contribute to these 7 5 = 35 types of knowledge maintenance. FixK SocialK QualityK Processing Activity MaintenanceKB PSM ExecutionKB SentenceK WordK BehaviouralK X W Y Z Y isa X Z may use X (and visa versa) KEY W can be partOf X maintained maintenanceKB executionKB by a second focus of most of current KA Figure 1: Maintenance KBs are a special kind of execution KBs which access more types of knowledge. Much of modern KA is focused only on execution KBsand preserve. These ve processing strategies and seven knowledge types de ne the 35 points of a knowledge management options space (see Figure 2). As we 45 move from acquiring wordK (\K" for knowledge) to preserving socialK (top-left to bottom-right of Figure 2), we see progressively less research work. Note the empty points in Figure 2 (marked by a \-"): these gaps are due to the recursive maintenance problem. Due to the fragmented and diverse nature of the KM eld, this article does 50 not aim to cover every KM system. Rather, we will mention systems that illustrate portions of the maintenance strategy option space 1 . Many of the systems reviewed here contribute to multiple places within the maintenance strategy option space. Hence, their descriptions may be broken up into di erent sections. Note that this article is \the view from NSW University" and hence 55 will have certain biases. For example:Much of modern KA focues on acquiring and operationalising PSMs.

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Flexible Knowledge Acquisition Through Explicit Representation of Knowledge Roles

Cited by 18 publications

References 10 publications

Collaborative ontology engineering: a survey

Collaborative ontology engineering: a survey

How task analysis can be used to derive and organize the knowledge for the control of autonomous vehicles

Knowledge maintenance: the state of the art

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