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Cognitive Support for Ontology Modeling

[document] Submitted on 27 August, 2019 - 13:07
Keywords Cognitive Support for Ontology Modeling Cognitive support knowledge engineering Information visualization requirements engineering
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It is possible to outline many different user types for ontology engineering. For
example, one might consider the actors to fill the roles of development manager, lead modeler, domain expert, expert user, and end user. Such a taxonomy is nonexhaustive and limiting. We prefer to abstract from the specifics, instead noting that while there are many actors involved in the development and use of an ontology, typically there are two major roles played (any of which may belong to different people at different stages). We characterize these as developers or modelers, and end-users. For example, from our work at the National Cancer Institute, the modeler role is played by the scientists who are concerned with the knowledge acquisition process, while the end user role is played by scientists who leverage the NCI Thesaurus at their own centres (for example, to provide terms for querying and report creation).

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Date published
UNKNOWN
Document type
technical white paper
Pages
30
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Paper-Ontology Modeling ijhcs-protege.pdf application/pdf   1.6 MB English DOWNLOAD!
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Abstract

Knowledge engineering tools are becoming ever more complex, and therefore increased cognitive support will be necessary to leverage the potential of those tools. Our paper motivates this claim by examining some previous work in this domain and explaining the nature of cognitive support. We discuss some of the problem areas we have encountered in our research. Through user questionnaires and observations carried out at the National Cancer
Institute (NCI) and the University of Washington Foundational Model of Anatomy (FMA) Project, we have begun to gain an understanding of the cognitive barriers experienced by the users of knowledge modelling tools. We analyze some existing solutions developed for the Prot´eg´e knowledge engineering tool using this understanding, one of which is our own tool, called Jambalaya. We then analyze the support Jambalaya provides using some nonfunctional design criteria and illustrate some trade-offs inherent in tool design. We suggest that the need for cognitive support in knowledge engineering is immediate and essential.

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