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It is common to find inconsistencies in knowledge bases that are being widely used.
Inconsistencies in knowledge bases are of major concern in knowledge representation and reasoning.
Beyond queries, we propose functional dependencies for graphs, to detect inconsistencies in knowledge bases and catch spams in social networks.
Dependencies provide us with one of few means to specify a fundamental part of the semantics of the data, and help us detect inconsistencies in knowledge bases and catch spams in social networks [29], among other things.
Inconsistencies in knowledge regarding recommended specimen collection suggest that respondents' may be unfamiliar with guidelines for testing.
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In artificial intelligence, it is important to handle and analyse inconsistency in 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.
By integrating the available data into a mathematical model that accurately represents the current understanding and comparing computational predictions to observations we sought to (a) identify inconsistencies in present knowledge, (b) propose resolutions, and (c) examine their functional implications.
In some cases, inconsistencies in the knowledge base indicate the use of modelling language features for purposes different from what may have been intended by the language designers.
We show that (mathsf {GFDs}) can be used as data quality rules and are capable of catching inconsistencies commonly found in knowledge bases, as violations of the (mathsf {GFDs}).
Measuring inconsistency has been considered as a necessary starting point to understand the nature of inconsistency in a knowledge base better.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com