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Knowledgebase approximation using association rule aggregation
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DOI：10.1007/s41060-021-00304-x.png)
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Abstract
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This paper introduces knowledgebase approximation and fusion using association rule aggregation as a means to facilitate accelerated insight induction from high-dimensional and disparate knowledgebases. There are two typical observations that make approximating knowledgebases of interest: (1) It is quite often that insights can be derived based from a partial set of the samples, and not necessarily from all of them; and (2) generally speaking, it is rare that the knowledge of interest is contained in one knowledgebase, but rather distributed among a disparate set of unidentical knowledgebases. As a matter of fact, the insights derivable from knowledgebases tend to be uncertain, even if they were to be derived from a wholistic analysis of the knowledgebase. Thus, optimal knowledgebase approximation may yield the computational efficiency benefit without necessarily compromising insight accuracy. This paper presents a novel method to approximate a set of knowledgebases based on association rule aggregation using the disjunctive pooling rule. We show that this method can reduce insight discovery time while maintaining approximation accuracy within a desirable level.
Keywords:
Knowledgebase approximation
Knowledge integration
Information fusion
Association rule mining
Disjunctive pooling
Dempster-Shafer theory
Computational complexity
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2.8
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1.1K
Citations:
1.3K
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