I’ve been spending the last few days playing with my favorite mental chew toy, the question “How do you ask for what you don’t know?” It’s an important question because every search engine query above a certain level of complexity involves filling in a knowledge gap. How you understand, define, and contextualize that gap means the difference between a successful and failed search.

Take the Olympics, which are happening shortly. Countless numbers of athletes are trying for team spots. I know little about current sports and nothing about the aspiring Olympic athletes. How can I learn more about who’s trying out, who did great, who’s making a buzz?

Wikipedia. And a lot of JavaScript.

I made a tool that starts with a keyword search for categories and a filter to identify pages for humans within that category (via looking for a couple of Wikidata properties.) The tool then evaluates the user-specified month/year pageviews for each page, divides the page listings into three tiers based on page views (the tiers are dynamically-generated) and shows the activity for the top three pages in each tier.

It takes a few minutes to run a big category like female American track and field athletes, but I’ve gone from 0 knowledge to learning about Karissa Schweizer’s performance in Oregon, Raven Saunders qualifying for their third Olympics, and Nia Akins’ domination of the 800 meter final. I don’t have Gossip Machine functionality yet with this tool; I’m just tossing the names into MegaGladys. That’s my next step.

It’s amazing how you can twist lists of information into interesting shapes just by applying the contexts of time and human interest.