Expert systems have traditionally captured the explicit knowledge of a single expert or source of expertise in order to automatically provide conclusions or classifications within a narrow problem domain. This is in stark contrast to social software which enables knowledge communities to share implicit knowledge of a more practical or experiential nature to inform individuals and groups to arrive at their own conclusions. Specialists are often needed to elicit and encode the knowledge in the case of expert systems, whereas one of the (claimed) hallmarks of social software and the Web 2.0 trend, such as Wikis and Blogs, is that everyone, anywhere can chose to contribute input. This openness in authoring and sharing content, however, tends to produce unstructured knowledge that is difficult to execute, reason over or automatically validate. This also poses limitations for its reuse. To facilitate the capture of knowledge-in-action which spans both explicit and tacit knowledge types, a knowledge engineering approach which offers Wiki-style collaboration is introduced. The approach extends a combined rule and case-based knowledge acquisition technique known as Multiple Classification Ripple Down Rules to allow multiple users to collaboratively view, define and refine a knowledge base over time and space.