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Scraping Wikipedia for knowledge graphs
Build smarter AI with structured data
Hey there, fellow tech enthusiast! Have you ever looked at the sheer volume of information out there and thought, “Wow, if only AI could truly understand all this?”
I know I have.
For decades, I’ve been wrestling with data, trying to make it behave and deliver insights. And let me tell you, when it comes to building truly smarter AI, it’s not just about more data; it’s about better data.
Specifically, structured data that AI can actually reason with.
That’s where knowledge graphs come into play, and guess what? One of the internet’s most comprehensive, free resources — Wikipedia — is a goldmine, just waiting for us to extract its hidden structured treasures.
What’s the big deal with knowledge graphs?
Imagine trying to understand a complex family tree just by looking at a list of names. You’d see “John,” “Mary,” “David,” but you wouldn’t know who’s married to whom, who’s a parent, or who’s a sibling.
It’s just a flat list. Now, imagine that same family tree drawn out with lines connecting everyone, labels on those lines saying “married to,” “parent of,” “sibling of.”
