Bojan Božić

Thanks for the interesting insights, Paco N. I must say I had no idea about the business side intricacies there. However, there is a proposed W3C Working Group Charter on Linked Data Signatures, that would make secure sharing possible. It’s not much at this stage, as the working group just started forming, but it might be worth keeping an eye on it: https://w3c.github.io/lds-wg-charter/index.html

Véronique G. It really doesn’t have to be shared understanding. The crucial points are the relations or properties (the edges). If they transport meaning and are not simply connecting nodes to visualise them, then you have a knowledge graph. Knowledge is information+context, which you clearly have, it’s also a graph representation. Voilà.

Hey Riccardo, I would say the simplest explanation would be: if all edges in your graph represent the same relation, it’s not a knowledge graph. So there needs to be inherent meaning that is represented in a graph. Your intuition was absolutely correct, as what we’re looking for in a knowledge graph is semantic meaning aka knowledge. Let’s say you have a graph representation of a network topology, this would not be a knowledge graph as every edge represents the same and there is no inherent knowledge that could be derived from it (only information). Hope that makes sense.

I wouldn’t call myself a “vector geek”, but the point I’m trying to make is: these terms are not interchangeable.

Glad to have a discussion on embeddings though at the conference 🙂

It has to do with dimensionality reduction, to get your (possibly sparse) high dimension vector space down to an embedded lower dimension and denser space (hence the proximity). I’m with you when it comes to naming things intuitively, but if I had to decide between being factual or intuitive, I’d go for the former, because embedding is the name for the proximity space and vectorisation is a too general term.

The thing is that vectorisation would be imprecise as real-valued vectors that encode the meaning of a word or subgraph such that the words or nodes that are closer in the vector space are expected to be similar in meaning. So the term embedding signalises this proximity in the vector space, while simply vectorisation would mean the creation of the space itsef.