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Incorporating Trust, Certainty and Importance of Information into Knowledge Processing Systems - An Approach

机译:将信任,确定性和信息重要性纳入知识处理系统-一种方法

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The origin of data (data provenance), should always be measured or categorized within the context of trusting the source of data. Can we be sure that the information we receive is trustworthy and reliable? Is the source trustable? Is the data certain? And how important is the received data the our current and next step of processing? We face these questions in the context of knowledge processing systems by developing a convenient approach to bring all these questions and values - trustability, certainty, importance - into a computable, measurable, and comparable way of expression. Not yet facing the question "How to compute trust or certainty?", but how to incorporate and process their measured values in knowledge processing systems to receive a representative view on the whole environment and its output.
机译:数据的来源(数据来源)应始终在信任数据源的上下文中进行测量或分类。我们能否确定收到的信息是可信赖的和可靠的?来源可信吗?数据确定吗?接收到的数据对我们当前和下一步的处理有多重要?我们通过开发一种方便的方法来将所有这些问题和价值观(可信赖性,确定性,重要性)转化为可计算,可衡量和可比较的表达方式,从而在知识处理系统的背景下面对这些问题。尚未面临“如何计算信任度或确定性?”的问题,而是如何在知识处理系统中合并和处理其测量值,以便获得有关整个环境及其输出的代表性视图。

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