Digitalization of knowledge work in communication-intensive domains such as intellectual property protection poses great challenges but also opportunities to improve today’s working environments. The legal domain is strongly characterized by knowledge work, whereby, despite a common legal framework, creativity of individual experts is decisive. This knowledge-intensive work deals with a great amount of data objects, not only as a working basis, but also as a result. While experts heavily follow individual working styles, they still rely on a vast amount of administrative tasks, which are carried out by the supporting staff. These tasks are expected to be performed regularly, reliably and without errors, despite necessary adjustments to the current case and the changing legal framework. Today, knowledge work and administrative tasks are typically supported by different tools that are hardly integrated. Therefore, the tracing of continuous work processes based on exchanged data objects is a great challenge. This traceability is crucial, not only for legal security reasons, but also to enable mining and learning of applicable knowledge about processes. In this paper, we propose a bottom-up approach, which applies a continuously evolving graph of integrated data objects and tasks to model and store static and dynamic aspects of administrative as well as knowledge work, and test the approach in a real-world setting in the domain of intellectual property. We further present initial results of a novel dependency-based mining approach to learn data-dependent task sequences in the graph-based model and discuss several methods for enabling privacy-preserving sharing and mining.

知识产权保护等通信密集型领域的知识工作数字化带来了巨大挑战，但也带来了改善当今工作环境的机会。法律领域的特点是知识工作，尽管有共同的法律框架，但个别专家的创造力是决定性的。这项知识密集型工作处理大量数据对象，不仅作为工作基础，而且作为结果。虽然专家们严重遵循个人的工作方式，但他们仍然依赖于由支持人员执行的大量行政任务。尽管对当前案件和不断变化的法律框架进行了必要的调整，但预计这些任务将定期、可靠且没有错误地执行。今天，知识工作和管理任务通常由难以集成的不同工具支持。因此，基于交换的数据对象跟踪连续工作过程是一个巨大的挑战。这种可追溯性至关重要，不仅出于法律安全原因，而且还有助于挖掘和学习有关流程的适用知识。在本文中，我们提出了一种自下而上的方法，该方法应用一个不断发展的集成数据对象和任务图来建模和存储管理和知识工作的静态和动态方面，并在实际环境中测试该方法在知识产权领域。