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This paper presents two approaches to measuring inconsistency for stratified knowledge bases.
A number of proposals have been proposed for measuring inconsistency for knowledge bases.
We apply the framework presented to the problem of measuring inconsistency in knowledge bases, putting forward relaxed forms for two debatable postulates for inconsistency measures.
Since we are measuring inconsistency, we score 1 if the condition is false, 0 otherwise.
Last but not the least, investigating the sources of heterogeneity as well as measuring inconsistency of the results across studies is a critical part of meta-analysis [ 48, 49].
Measuring inconsistency has been considered as a necessary starting point to understand the nature of inconsistency in a knowledge base better.
Similar(53)
The authors say that " current methods for estimating HGT rely on measuring inconsistencies between sequence trees or looking for unusual compositional features, so there is no way for them to distinguish between innovations and displacements".
Current methods for estimating HGT rely on measuring inconsistencies between sequence trees or looking for unusual compositional features [ 16], so there is no way for them to distinguish between innovations and displacements.
This model measures inconsistency in terms of obsoleteness in the data with number of missed updates.
We tested for heterogeneity with the Cochrane Q test and measured inconsistency (I2; the percentage of total variance across studies that is due to heterogeneity rather than chance) of treatment effects across all clinical endpoints and averse effects [22], [23].
Additionally, the I statistic was used to measure inconsistency across studies and represents the proportion of variability in the estimates that is due to between-study variation (40).
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