Measures of uncertainty for knowledge bases

Uncover the reasons for performance differences between measurement functions (Provably)

“…Therefore, how to measure the uncertainty of a system plays a vital role in machine learning, data analysis, artificial intelligence applications, and cognitive science [ 6 ]. The current mainstream method is to use r ough s et t heory (RST) [ 18 ] to measure the uncertainty of KBs [ 1 , 19 ]. RST, as a powerful tool that effectively measures the uncertainty of KBs, has attracted more and more attention from artificial intelligence practitioners, such as decision-making [ 20 , 21 ], computer-aided diagnosis [ 22 ], attribute reduction [ 23 ], decision analysis [ 24 , 25 ], and predicting the COVID-19 cases [ 26 ].…”

“…For instance, measurement based on the combination of information entropy and rough sets [ 29 ]; Using rough entropy theory to measure the uncertainty of KBs [ 30 ]; Measurement based on the combination of knowledge granulation and rough sets [ 31 , 32 ]. Especially in recent work, many scholars focus on the method based on knowledge structure [ 33 ] to measure the uncertainty of knowledge bases [ 1 , 19 ]. And obtain many exciting conclusions through a lot of experiments.…”

“… Conclusions are often based on the verification of a limited number of data sets, lacking a solid and comprehensive theoretical guarantee. For example, recently, an exciting experimental conclusion in [ 1 ] about measures of uncertainty for the KBs has attracted great research interest for scholars. In [ 1 ], the authors select three data sets and conduct numerical experiments on these three data sets to verify the superiority of using the knowledge amount to measure the uncertainty of the KBs.…”

“…For example, recently, an exciting experimental conclusion in [ 1 ] about measures of uncertainty for the KBs has attracted great research interest for scholars. In [ 1 ], the authors select three data sets and conduct numerical experiments on these three data sets to verify the superiority of using the knowledge amount to measure the uncertainty of the KBs. 1 However, these successful conclusions lack perfect mathematical expression and interpretability.…”

“… Recently, an exciting experimental conclusion in Li et al (Knowl Inf Syst 62(2):611–637, 1 ) about measures of uncertainty for knowledge bases has attracted great research interest for many scholars. However, these efforts lack solid theoretical interpretations for the experimental conclusion.…”