Intentional forgetting through cognitive-computational methods of priorization, knowledge compression and contraction

在组织的背景下，知识结构可以被认为是hyperthymestic，即，所存储的信息对于用户来说是无差别的，信息量单调增长，并且不发生知识缩减或紧致化。因此，它需要越来越多的时间来整理过时的，无关的或很少使用的信息。特别是对于大量的数据，这个过程需要大量的时间。这个项目的目标是减少这种费力的预选和信息聚合导致用户的工作量，通过使用认知科学和计算机科学的方法。出发点是基于组织中的知识结构的分析，在认知架构中人类记忆结构的数学和心理建模方法的分析，并开发优先级和遗忘功能，这可能有助于压缩和减少不断增加的数据量。伴随着知识表示的计算方法，我们开发了一个遗忘的认知计算系统。这种模型提供了系统地确定和调整系统模型参数的机会，并使它们对每一个知识结构都是透明的。然后评估这个遗忘模型是否适合精益工作流程，并在组织测试用例中进行重新调整。

In the context of organizations knowledge structures can be considered as hyperthymestic, i.e., stored information is indistinctively available for the user, the amount of information grows monotonically, and no knowledge reduction or compactification takes place. As a result, it requires more and more time to sort out information that is outdated, irrelevant, or rarely used. Especially for large amounts of data this process requires an enormous amount of time.This project's goal is the reduction of this effortful preselection and aggregation of information leading to user's working load by using methods from cognitive science and computer science. The starting point is based on the analysis of knowledge structures in organizations, the analysis of mathematical and psychological modeling approaches of human memory structures in cognitive architectures, and to develop functions for priorization and forgetting that may help to compress and reduce the increasing amount of data. Accompanied by computational methods from knowledge representation a cognitive computational system for forgetting is developed. Such a model offers the opportunity to determine and adapt system model parameters systematically and makes them transparent for every single knowledge structure. This model for forgetting is then evaluated for its fit to a lean workflow and readjusted in an organizational test case.