A knowledge-based framework for the management of imprecision in data

不精确是许多数据固有的，特别是在复杂的生物、生态和医学系统中。通常，现实生活中的决策是基于主观的、不确定的和不精确的数据做出的。因此，不精确度的管理是数据共享、决策、信息披露和数据挖掘中最关键的问题之一。在几个学科的背景下对不精确进行了研究，并开发了各种方法来表示数据库中的不精确数据，最近还在Web存储库中进行了表示。然而，目前的方法没有充分解决在领域知识的背景下对不精确的解释。然而，对不精确的解释高度依赖于语境。例如，“血压高于正常”的说法在一种情况下可能足够精确，或者在另一种情况下可能不够精确，可能需要诸如“160/90”之类的测量和附加信息“无需抗高血压药物”。

Imprecision is intrinsic to many data, especially in complex biological, ecological, and medical systems. Often, real-life decisions are made based on subjective, uncertain, and imprecise data. Thus, the management of imprecision is one of the most critical issues in data sharing, decision making, information disclosure, and data mining. Imprecision has been studied in the context of several disciplines and various methods have been developed to represent imprecise data in databases, and, recently, in the Web repositories. However, the current approaches do not address sufficiently the interpretation of imprecision in the context of domain knowledge. Yet, the interpretation of imprecision is highly contextual. For example, a statement "blood pressure above normal" may be sufficiently precise in one context or it could be insufficient in another context and a measurement such as "160/90" and additional information "no antihypertensive medication" may be needed.