Years ago, I wrote a puzzlehunt puzzle that involved navigating through words where an edge existed if the two words formed a common 2-gram (that is, they often appeared one after another in a text dump of Wikipedia).
For example, a fragment of the graph from the puzzle is: mit -> press -> office <- post <- blog.
This work is obviously much more advanced, and it's very cool to see that they managed to make it work with semantic connections. I was able to get away with a much simpler approach since I only cared about 2-grams over a set of about 1000 words (I literally used a grep command over the entire text of the English wikipedia; it took about a day to run).
But the core idea is shared: 1) wanting to build a graph representation of word connections for a puzzle, 2) it being way to much work to do that manually, 3) you would miss a bunch of edges if you did do it manually, so 4) use programming tools to construct a dataset, and then 5) the end result is surprisingly fun for the user because the dataset is comprehensive and it feels really natural.
If anyone is curious, the puzzlehunt puzzle is here: https://dhashe.com/files/puzzles/word-wide-web.pdf
And the solution is here: https://dhashe.com/files/puzzles/word-wide-web-sol.pdf
And a fair warning to anyone unfamiliar with puzzlehunt puzzles: they do not come with instructions and it is very common to get stuck when solving them, especially when solving them alone. You have not completely solved a puzzlehunt puzzle until you extract an answer word or phrase from the puzzle. This one has an extra layer after filling in the words in the graph. Peeking at the solution is encouraged if you get stuck.