I have not had much time to ‘techy things’ due to writing up a thesis, finding myself this week with a few hours on a train try something new. I decided , wanted to look at some data tools and techniques that had been on my radar for a little while. I began by poking about some stuff on my list that began with K: Kaggle, Knime and Keras. Being honest, I found looking through examples of getting into these technologies very uninspiring and boring; I’m not currently sure if I am ‘burnt out’ with technology or if I am ‘burnt out’ with examples that don’t interest me. Trying to find something interesting to do I looked back on some of the things I had played with previously. I have found that the most interesting data is on Wikipedia, and have been fond of using DBpedia to extract interesting datasets. However, Wikipedia has rules about what should and shouldn’t be on there, and articles must be “worthy of notice”.
Looking for something interesting to poke about, I wondered where I would find things “not worthy of notice”. Perhaps there is a shitty Wikipedia with articles that nobody except the most hardcore enthusiasts cares about?
It turns out there is; start started exploring Fandom.com ,a website that hosts lots of Mediawiki instances for fans of obscure things, and decided I wanted to start to mine this information in there, but fell into a few traps. Structured data still exists in the infoboxes, but:
- There is no DBpedia or Wikidata for fandom, so there is no way to query the data with sparql etc
- The Mediawiki API seems to have been tampered with. Or at least I couldn’t get wptools to work properly. Wptools does support custom endpoints now, but it broke for me and I didn’t have the patience to work out why. Help would be appreciated if anybody does use wptools with fandom
Anyway, I wrote a very quick python script that will extract all the items in a category, then loop through them and scrape information from all their infoboxes. It then writes this to a JSON file. The script is in GitHub: It is very quick and dirty, so you actually have to change the URL on to the category you want before running the code. Due to the never-ending pitfalls of web scraping, I don’t think I will update and maintain this but will look for other ways into the data; being a bunch of Mediawiki instances, it must be there!
The data is saved in a JSON file; each infobox item is the key and result is the value. If a BR tag or something splits the value then it will be converted into an array.