Knowledge workers are confronted with a massive load of data from numerous heterogeneous sources, making it difficult for them to identify the information relevant for performing their tasks. Particularly challenging is the alignment of information with business processes. In previous work, we introduced a Semantic Network (SN) for bridging this gap, i.e., for identifying relations between information and business processes. What has been neglected so far is the maintenance of an SN in order to keep the SN consistent, complete, and up-to-date. This paper tackles this issue and extends our approach with algorithms dealing with the maintenance of an SN. For this purpose, we identify and classify properties of objects and relations captured in an SN and show how these properties can be maintained. We use a case from the automotive domain in order to demonstrate and validate the feasibility and applicability of our maintenance framework.