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Issue No.06 - December (1993 vol.5)
pp: 965-969
<p>The problem of making decisions among propositions based on both uncertain data items and arguments which are not certain is addressed. The primary knowledge discovery issue addressed is a classification problem: which classification does the available evidence support? The method investigated seeks to exploit information available from conventional database systems, namely, the integrity assertions or data dependency information contained in the database. This information allows ranking arguments in terms of their strengths. As a step in the process of discovering classification knowledge, using a database as a secondary knowledge discovery exercise, latent knowledge pertinent to arguments of relevance to the purpose at hand is explicated. This is called evidence. Information is requested via user prompts from an evidential reasoner. It is fed as evidence to the reasoner. An object-oriented structure for managing evidence is used to model the conclusion space and to reflect the evidence structure. The implementation of the evidence structure and an example of its use are outlined.</p>
data properties; uncertain data items; knowledge discovery issue; classification problem; conventional database systems; integrity assertions; data dependency information; secondary knowledge discovery exercise; latent knowledge; user prompts; evidential reasoner; object-oriented structure; conclusion space modelling; evidence structure; data integrity; case-based reasoning; classification; data integrity; database theory; deductive databases; knowledge acquisition; object-oriented databases
D.A. Bell, "From Data Properties to Evidence", IEEE Transactions on Knowledge & Data Engineering, vol.5, no. 6, pp. 965-969, December 1993, doi:10.1109/69.250078
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