out = wikimark("""Peter Hintjens wrote about the relation between technology and culture.
Without using a scientifical tone of state-of-the-art review of the anthroposcene antropology,
he gives a fair amount of food for thought. According to Hintjens, technology is doomed to
become cheap. As matter of fact, intelligence tools will become more and more accessible which
will trigger a revolution to rebalance forces in society.""")

for category, score in out:
    print('{} ~ {}'.format(category, score))

And the output would be:

Art ~ 0.2
Science ~ 0.8
Society ~ 0.4

That is the goal of the project but were are not there yet.

I read classification in encyclopedia terms is complex matter but I have settled on wikimedia vital articles on run experiments on level 3 which are encouraging.

Here is the output of my program before post processing:

$ curl https://github.com/cultureandempire/cultureandempire.github.io/blob/master/culture.md | ./wikimark.py guess build/
similarity
 +-- Technology ~ 0.09932770275501317
 |   +-- General ~ 0.09932770275501317
 +-- Science ~ 0.09905069171042175
 |   +-- General ~ 0.09905069171042175
 +-- Geography ~ 0.09897996204391411
 |   +-- Continents and regions ~ 0.09914627336640339
 |   +-- General ~ 0.09881365072142484
 +-- Mathematics ~ 0.09897542847422805
 |   +-- Other ~ 0.09911568655298664
 |   +-- Arithmetic ~ 0.09883517039546945
 +-- Society and social sciences ~ 0.09886767613461538
 |   +-- Social issues ~ 0.09886767613461538
 +-- History ~ 0.09886377525104235
     +-- General ~ 0.09893293240770612
     +-- History by subject matter ~ 0.09884456012696491
     +-- Post-classical history ~ 0.09881383321845605

The algorithm selected the 10 most relevant subcategories out of 73.

Now I need to scale this to level 5. But it poorly organized.

Can wikidata help wikipedia vital articles?

ref: https://en.wikipedia.org/wiki/Wikipedia_talk:Vital_articles#Wikidata_integration_to_help

ref: https://github.com/amirouche/sensimark