The title, "A New Way to Store Knowledge", indicates this is a joke.
You know it is when it starts like this: "...All tabular knowledge can be stored in a single long plain text file. The only syntax characters needed are spaces and newlines."
That's fundamentally the simplest way of storing text. And it's nothing new, yet people have long ignored that simplicity for much more complicated ways of storing text.
I don't see it as a this-is-all-a-joke thing though, more tongue in cheek.
also I think one-big-text-file has a certain simplicity, like everything-is-a-file on unix (or more properly plan9)
;)
Thank you for the feedback. I've updated the paper with some more links.
The language in which the measures are written in (currently called Grammar. I will like rename it to something like Parssers) is quite advanced.
The improvements over Recutils, the closest precursor I am aware of, have now been added.
The PLDB ScrollSet is now about 500,000 cells of information. Each cell is strongly typed and fully auditable by git. There is a high amount of signal in that dataset. It is an intelligent set of weights, and continually getting more intelligent. And it is read at runtime as a single plain text file and compiled to a single CSV (or tsv, json, etc).
All from using the system documented in the paper (and the advanced language for Parsers).
If you can point me to a similar database or similar scale anywhere in the world (plain text base, >10e5 size, git backed, strongly typed, hierarchical and graphical), I would be grateful as I might learn something.