No, I don't think you have missed anything from the overview. Yes, you're right... the process to construct and maintain the knowledge graph is not part of the application. In that respect, I have been considering several related features to help the user with that process: 1) topic map templates that allow for very quick creation of a predefined taxonomy and 2) machine-learning for auto-tagging and recommendations for associations.
Although the above features do not make for the complete process you are referring to, at least they assist users as part of that process.
Interesting ideas, however it still sounds like you're tackling knowledge as if it's a fixed point in time. That's good to do when you're starting to build your knowledge base, but there's a stage of understanding beyond that which I'm trying to define and quantify.
As a knowledge manager, one of my biggest issues is the temporal nature of knowledge and conveying the cyclical nature of inputs and outputs to people. The "clean" process is often the exception in the real world, and short circuiting often occurs throughout the knowledge development process.
I don't think it really becomes apparent until you've been looking at the same knowledge for a while and start to see there's a pattern above the pattern. Changes in one part of the graph seem to be connected to changes in another part of the graph.
I'm searching for a way to visualize this extra dimension of data. I don't know if graph edges are complex enough to show the dimensionality of the change.
Perhaps a simple time-lapse overview of a graph would provide what I'm looking for. Or maybe there's a temporal relational layer that sits above topics and resources. I'm not sure.
In a previous implementation of Contextualise I included the ability to timestamp topics allowing for a straight-forward temporal navigation of topics using a timeline component. It was somewhat naive, but it did allow you to navigate a knowledge graph from a time perspective. This time around I probably wouldn't add the timestamp directly to the topic but instead tackle this by means of temporal relationships using, obviously, associations. The outcome would be (regardless of how it is actually modeled in the topic map) is that a timeline component could be appropriately populated allowing for temporal navigation of a knowledge graph.
Although the above features do not make for the complete process you are referring to, at least they assist users as part of that process.
Food for thought.