Yep, ConceptNet Numberbatch is my work too, and it's been the most effective way to show that knowledge graphs matter -- that there is more to know about word relationships than you can get from distributional semantics ("word2vec") alone.

Oh really? Very nice... although I'm only using the aligned distributional semantic nature of them.

I have some background in question answering over knowledge graphs, though, so I'm familiar with their strengths.

I'd be interested to hear about what you're doing with it.

In my company Luminoso's work, it's important in building domain-specific models that can be used for topic detection, search, and classification. Beyond that, I use it for mostly the basic demos -- word similarity, text similarity, analogies, et cetera.

I believe based on its performance there that it should be a pure upgrade to the kind of applications that use word2vec, but I'd like to know what particular applications it's being used in besides my own.