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Bridging knowledge graphs to understand our world

Published onApr 15, 2019
Bridging knowledge graphs to understand our world

Lately I have been dreaming of knowledge graphs, iteratively refined and detailed, that allow us to pore over what we know and enhance our understanding. A framework and language for amplifying, amending, annotating, qualifying, contextualizing, decomposing, reconstituting, synthesizing and comparing specific and uniquely-named elements of trains of logic, thought, computation, interpolation, and other inference.

One shared element that keeps appearing is an underlayer of data, assertions, and reported knowledge, designed to support many different mesh sizes (for the conceptual mesh used to describe an observation), to let you zoom into increasingly granular bits of observations, and to add context and background, tracing each element back to original observations. To make its use efficient, this would also support clusterings (equivalence classes of names that, in a given context, resolve to the same thing), and filters (for deciding what to include or exclude in a given view).

For this, I propose a collective project to which we can all contribute: an Underlay, comprised of graphs of interlinked, structured data points. Each point versioned, meshed, linked to its sources, and linking likewise to the composites and analyses that have relied on it. Each graph a composite of many different layers, each layer with its own canonical mesh-grain. And the underarching Underlay project a constellation of individual graphs, describing how they align with one another, providing a way to name and disambiguate an idea or claim or discussion across the connected whole.

Revised from The Longest Now.

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