Structuring Collective Knowledge, v2 : Interpreting Layered Knowledge
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Codex Editor : paper
Go-links : can this be made global? A global authority file
Coronavirus : collecting data on those who have passed away
ImageNet : human annotations of human images; linked to WordNet
WordNet: grouped words into synsets (concepts)
Images associated to 20k synsets, via URLs (imgs not owned), tagged
~> used to train ResNet, SENet, BigGAN —> inception
Visual concept vocabulary (like a “map” of GAN latent space) — SS, SG
How to structure data from collective labelling —> Toward a Topology of Visual Experience
Annotated Hub of Science — Amr, SJ, SG, EI
(approaches to hypertexts + research)
Recipes + Food sustainability — EI, SJ
(important + good model, clear requirements)
Meta - wikilinks + query language (start at root, apply constraints)
(what do we need to streamline this collab)
SG: Specs for network graphs or … hierarchical datasets? When you click on something: be able to render arbitrary doc-set or html snippet? I’ve been thinking about latent space for a while; in the uni, people talk about getting meaningful info out of a neural net / that’s network data of its own, that could use complex viz.
AC: that’s really interesting — kg problems are often sth that sits b/t a standard network graph and a more structured thing.
Choosing viz and hierarchy models?
Keyword branching out into other ways.
How to specify a choice of graph viz along w/ what you render on mouseover/clicking on nodes?
Let users shove away information / bring relevant info to the front.
Neo4j : can we improve on both the defaults and how we interact with them?
New proposal: capturing graph-viz approaches + styles?
code : a list of awesome graph viz? [awesome lists as meta-layer]
HUDs and GUIs: annotated reviews of designs in fiction (film)
“Let’s do better than force-directed graphs”
Working w/ public knowledge graphs, building new visualizations + overlays
Room: Andra, Blake, FK, ET, SG, AC, SS, SK
You don’t always know what you mean to ask until you start seeing some answers. Example of that — Google Translate (showing potential results, letting you refine the result based on possible alternatives for each part of a paragraph)
Postive questions to answer:
What Q’s / queries do you want to answering?
What datasets are you using?
What audiences might use this, or contribute to it?
What results in the world (here: regulations, food guidance) might need it?
Are there natural focused subsets to develop along the way?
Think about a single Overlay, a few Underlay collections, and an Interlay spec that could link them / handle any interchange + interfacing needed.
What does it take for data currently under license / not being shared to become usable in this way?
What curation and updating are happening? are needed?
What steps can be iterated? Where there are obstacles, is there a related step that is easier to start with (at another level of abstraction or access, w/ a similar source or topic)? Clearly describing an obstacle is a first step towards working around it.
Network: How does this fit into other popular / existing / needed sources?
To what degree can we use half-closed data to make tools for the user?
You can always get metadata or analyze closed data and share that.
Countering the zero-sum game narrative: Using a model to derive the optimal health outcomes is actually very similar to a model that optimises economic outcomes: you have an optimisation question where you can minimise the summation of costs due to both cases and restrictions
source — Oxford tool: http://epidemicforecasting.com allows you to modulate different aspects that affect the reproduction rate
More difficult to find data on what different COVID measures will cost. Can also account for vaccination models, including staging etc.
Where is the data?
‘Towards an optimal policy dashboard’ — want an idea of costs and cases caused by different policies.
SJ: Q: How much do your choice of data sources, derivatives (these visuals), and audiences affect how effective this work is? [how effective it feels; empirically the feedback you get, if appropriate]