In knowledge-based organizations, workers need task-relevant knowledge and documents to support their task performance. A knowledge flow (KF) represents the flow of an individual's or group members' knowledge-needs and the referencing sequence of documents in the performance of tasks. Through knowledge flows, organizations can provide task-relevant knowledge to workers to fulfill their knowledge-needs. Nevertheless, in a collaborative environment, workers usually have different knowledge-needs in accordance with their individual task functions. Conventional KF models do not provide workers with the different views of a knowledge flow that they require to meet these knowledge-needs. Several researchers have investigated KF models but they did not address the concept of the knowledge-flow view (KFV). This study proposes a theoretical model of the KFV using innovative methods. Basically, a KFV is a virtual knowledge flow derived from a base knowledge flow that abstracts knowledge concepts for individual workers based on their knowledge-needs. The KFV model in this study builds knowledge-flow views by abstracting knowledge nodes in a base knowledge flow to generate corresponding virtual knowledge nodes through an order-preserving approach and a knowledge concept generalization mechanism. The knowledge-flow views not only fulfill workers' different knowledge-needs but also facilitate knowledge support in teamwork.