Graph-grammar assistance for knowledge acquisition

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abstract: One of the most difficult aspects of modeling complex medical dilemmas in decision-analytic terms is composing a diagram of relevance relations from a set of domain concepts. The concepts, as presented to the knowledge engineer, generally have no inherent order, yet they have charcteristic positions and typical roles in a semantic network model. We have been using a graph-grammar production system to express such inherent interrelationships among medical terms. We have found that this graph-grammar system facilitates the modeling of medical dilemmas. We also suggest a translate-and-assemble perspective for certain knowledge-acquisition problems. We suspect that graph grammars,and the translate-and-assemble perspective, may be useful guides to modeling in many other circumscribed domains.

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AbstractOne of the most difficult aspects of model One of the most difficult aspects of modeling complex medical dilemmas in decision-analytic terms is composing a diagram of relevance relations from a set of domain concepts. The concepts, as presented to the knowledge engineer, generally have no inherent order, yet they have charcteristic positions and typical roles in a semantic network model. We have been using a graph-grammar production system to express such inherent interrelationships among medical terms. We have found that this graph-grammar system facilitates the modeling of medical dilemmas. We also suggest a translate-and-assemble perspective for certain knowledge-acquisition problems. We suspect that graph grammars,and the translate-and-assemble perspective, may be useful guides to modeling in many other circumscribed domains. eling in many other circumscribed domains.
AddressStanford, CA, USA  +
AuthorJohn W. Egar  +
Bibtypetechreport  +
InstitutionKnowledge Systems, AI Laboratory  +
KeyKSL-92-37  +
MonthApril  +
NumberKSL-92-37  +
TagComputer science  +
TitleGraph-Grammar Assistance for Knowledge Acquisition  +
Tr idKSL-92-37  +
Year1992  +
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