DISCO, European Dictionary of Skills and Competences, offers the user a tree to be searched or browsed. Inspecting the tree nodes, we see that concepts are contained in LI elements with an liItem class. Executing $(‘.liItem’).length in the console we get 676. They claim instead to collect more than 104000 concepts. A bold claim?
A better look at the tree structure reveals that some concepts have a data-loaded attribute set to true and some set to false. In particular, true denotes readily available nodes (downloaded with the initial page load) and false denotes nodes that require an AJAX call before being displayed. Leaf nodes are always of the former kind, but internal nodes can be of both kinds. Would we get those 104000 concepts if we unfolded all the false nodes?
We’ll try. Along the way we’ll also store all nodes into a different structure, something more portable than bare HTML. JSON seems a good option. Ironically, DISCO uses getJSON to download HTML snippets. To summarize, we are now going to store all the HTML tree of DISCO into a JSON structure.
Too bad for me. At least it was fun to make DISCO’s page behave outside of DISCO’s server.
Here is the snippet I came up with:
There are only a few things to note:
- I’ve put a limit as a guard to safely try things out
- with recursive structures –like trees– it’s very useful to limit actions to a small amount of nodes before going full monty
- this limit is just how many nodes to visit, you can start with a low number like 20 or 50 and see how it works
- you should get quite a long list of messages output to the console, and if all was fine, the last message will be the result
- The result is a hash of node ids as keys and node objects as values
- for example, window.disco.nodes is
which corresponds to this node in DISCO’s page
- for example, window.disco.nodes is
- The functions download(node) and download_children(node, children) are mutually recursive
- their arguments are coherent, i.e. node is an LI element and children is an array of LI elements
- the latter is not integrated into the former because we need to provide the same treatment to both children readily available and those that will be in the future
- they start visiting from the two roots –horizontal_skills and vertical_skills– and drill down into the tree structure
- The UI is never updated by the snippet, instead all the state is automatically kept in memory by the recursive descent
- if you unfolded aesthetic sensitivity (node 16091) in the tree yourself between two executions with a small number of nodes (say 20), you’d get two different results
- the first result would (probably) not show aesthetic sensitivity children while the second result would (probably) not show the last two nodes of the first result, thus keeping the number of nodes stuck to the given limit
- if you want to go back to the initial mint state, a simple reloading won’t be enough without deleting first the session cookie
- Finally, you can run JSON.stringify(window.disco) and get a nice JSON string which you can copy and paste somewhere and save to a file
- the hash to string conversion is gonna need some minutes… so many in fact that I left the browser working “indefinitely” (half an hour?)
- the resulting string is humungous too: 3.785.133 bytes (3,8 MB on disk).
The execution of the above snippet with a limit of 105000 nodes takes around 3 minutes on my MacBook Air with 4GB RAM. At the end, you’ll discover that the last node was number 7380 !!
Wow, that’s a huge difference from the claimed “more than 104000 concepts”. How can it be?
Even considering that they provide a multilingual thesauri with 11 languages and they could have inflated 7380 * 11 times = 81180, there is still around 28% of missing concepts. Could they have added the number of phrases? No, because they separately claim “approximately 36000 example phrases”. They could have instead added the number of synonyms.
Running the above code we get 3443 synonyms, which added to 7380 concepts make for 10823 terms, which inflated 11 times make for 119053 terms in all languages.
- 7380 * 11 + 3443 + X * 10 = 104000
- X = (104000 – 84623) / 10
- X = 1937.7 = 56% * 3443 = 26% * 7380
Hm, I don’t know. It seems to me that they “confused” concepts with terms and at the same time, while English synonyms count is around 47% of concepts, in all other languages synonyms count is around 26% of concepts, which is ostensibly much less.
All in all, 7380 concepts is a good number but it’s only the 7% of what they claim.