Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In b-scale semantic data analytics there is a great need for methods which aggregate uncertainty claims, on the one hand respecting the information provided as accurately as possible, while on the other still being tractable. Traditional statistical methods are more robust, but only represent distributional, additive uncertainty. Generalized information theory methods, including fuzzy systems and Dempster-Shafer (DS) evidence theory, represent multiple forms of uncertainty, but are computationally and methodologically difficult.  require methods which provide an effective balance beten the complete representation of the full complexity of uncertainty claims in their interaction, while satisfying the needs of both computational complexity and human cognition. Here  build on J{\o}sang's subjective logic to posit methods in focused belief measures (FBMs), where a full DS structure is focused to a single event. The resulting ternary logical structure is posited to be able to capture the minimal amount of generalized complexity needed at a maximum of computational efficiency.  demonstrate the efficacy of this approach in a b ingest experiment over the 2012 Billion Triple dataset from the Semantic b Challenge.
['Cliff Joslyn', 'Jesse Weaver']
I am writing a research paper on a idea Focused Belief Measures for Uncertainty Quantification in High Performance Semantic Analysis
[{"from": "human", "value": "I am writing a research paper on a idea Focused Belief Measures for Uncertainty Quantification in High Performance Semantic Analysis"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In b-scale semantic data analytics there is a great need for methods which aggregate uncertainty claims, on the one hand respecting the information provided as accurately as possible, while on the other still being tractable. Traditional statistical methods are more robust, but only represent distributional, additive uncertainty. Generalized information theory methods, including fuzzy systems and Dempster-Shafer (DS) evidence theory, represent multiple forms of uncertainty, but are computationally and methodologically difficult.  require methods which provide an effective balance beten the complete representation of the full complexity of uncertainty claims in their interaction, while satisfying the needs of both computational complexity and human cognition. Here  build on J{\\o}sang's subjective logic to posit methods in focused belief measures (FBMs), where a full DS structure is focused to a single event. The resulting ternary logical structure is posited to be able to capture the minimal amount of generalized complexity needed at a maximum of computational efficiency.  demonstrate the efficacy of this approach in a b ingest experiment over the 2012 Billion Triple dataset from the Semantic b Challenge."}]