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RA: A memory organization to model the evolution of scientific knowledge

This dissertation addresses the dichotomy between semantic and episodic knowledge by focusing on the evolution of scientific knowledge. Even timeless scientific knowledge about the nature of the world accrues only through discrete episodes, with each scientist building upon the work of his/her predecessors. Hence, a memory organization to model the knowledge of a scientific field should reflect not only the knowledge pertaining to the field, but also the knowledge pertaining to the evolution of the field. A computer program called RA is described: RA proposes a memory organization for scientific knowledge in terms of a representational idea called Research Schemas. Research Schemas view research papers, not as isolated pieces of text, but as related episodes that contribute to the growth of a scientific discipline. This memory organization is validated by showing that it supports a number of different capabilities: it enables RA to suggest new research directions, acquire new research schemas, retrieve papers that have similar research strategies, and generate both chronological and analogical summaries of research papers. A combination of these capabilities constitutes a framework for 'Computer-Aided Research.' The RA system also includes a learning technique to acquire new research schemas. While similarity-based techniques use multiple examples (and some form of encoded bias) and explanation-based techniques use a domain theory as the basis for generalization, there is no apparent basis for RA's generalization. An analysis of RA's learning strategy shows that the category structure of RA's world provides a basis for its generalization: RA generalizes instantiations into categories that are both associative and discriminative. Interestingly, this turns out to be precisely the property that characterizes basic-level categories that have been studied by psychologists. This dissertation explores the implication of this results to learning and knowledge representation.

Identiferoai:union.ndltd.org:UMASS/oai:scholarworks.umass.edu:dissertations-7712
Date01 January 1990
CreatorsSwaminathan, Kishore S
PublisherScholarWorks@UMass Amherst
Source SetsUniversity of Massachusetts, Amherst
LanguageEnglish
Detected LanguageEnglish
Typetext
SourceDoctoral Dissertations Available from Proquest

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