Just a weird idea.

It is very interesting how neural nets can caption images. Quite interesting. It is done by building a state-model of the image, that is feed into a kind of neural net (RNN) and that net (a black box) will transform the state-model into running text. In some cases the neural net is steered. That is called an attention control, and it creates relationship between parts in the image.

Swap out the image wit an item, and a virtually identical setup can generate captions for items. The caption for an item is whats called the description in Wikidata. It is also the first sentence with a lead-in in Wikipedia articles. It is possible to steer the attention, that is to tell the network what items should be used, and thus the later sentences will be meaningful.

What that means is that we could create meaningful stub entries for the article placeholder, that is the "AboutTopic" special page. We can't automate this for very small projects, but somewhere between small and mid sized languages it will start to make sense.

To make this work we need some very special knowledge, which we probably don't have, like how to turn an item into a state-model by using the highly specialized rdf2vec algorithm (hello Copenhagen) and verifying the stateful language model (hello Helsinki and Tromsø).

I wonder if the only real problems are what do the community want, and what is the acceptable error limit.

John Erling Blad /jeblad