Graphs (networks, webs) are a data structure we have not exploited enough. Tons of research is going into using graphs for artificial intelligence, and for understanding human intelligence (as in this article). But what about using knowledge graphs to augment human intelligence, like a prosthetic?

This is the power of Google search. It uses a knowledge graph that models the world (with an emphasis on the internet). The graph is big, but the view of it offered to users is minuscule -- in part to keep the interface as simple as possible, and in part for economic reasons.

There is open source software that lets people keep their own knowledge graphs. In Semantic Synchrony [1] you can keep a knowledge graph and merge it with others' knowledge graph. Joshua Shinavier (who wrote Semantic Synchrony) and I share a graph with over 400,000 nodes, and most views load in the blink of an eye.

A sister project, Digraphs with Text[2], offers a more flexible system of expression: It generalizes the graph, allowing relationships to involve more than two members, and allowing relationships to be members of other relationships. It also offers a search facility very much like natural language: To search, for instance, for reasons neurons need vitamin B, you would use a query like "(neurons #need vitamin B) #because /it". (The # mark indicates a joint between members of a relationship.)

[1] https://github.com/synchrony/smsn/wiki/ [2] https://github.com/JeffreyBenjaminBrown/digraphs-with-text