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Research Article
Pouya Mehrannia
University of Waterloo Faculty of Engineering
Corresponding Author
ORCiD: https://orcid.org/0000-0002-9567-685X
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This work is licensed under a CC BY 4.0 License. Read Full License
This paper introduces knowledgebase approximation and fusion using association rules 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
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This is a preprint. It has not completed peer review.
Research Article
Pouya Mehrannia
University of Waterloo Faculty of Engineering
Corresponding Author
ORCiD: https://orcid.org/0000-0002-9567-685X
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 08 Mar, 2021
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Analysis
License:
This work is licensed under a CC BY 4.0 License. Read Full License
This paper introduces knowledgebase approximation and fusion using association rules 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.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
This preprint is available for download as a PDF.
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