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Quantifying Accuracy Dimension within Available Context

We propose a novel approach to automatically quantify accuracy within context (QAC). Accuracy can be gauged by discrepancy between data view and its entity's perfect representation. Since it is difficult to obtain the perfect representation of entity, we propose to approximate perfect representation within its available context and accuracy can be quantified in the context. By naturally borrowing entropy concepts from information theory, the measure is easily given for different types of data. In this way accuracy is properly quantified. Our QAC approach can not only give an objective score and ranking in a cooperative multi-source environment but also avoid human's laborious interaction. Theory and experiment shows our approach performs well for quantifying accuracy.