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Comment by mark_l_watson | Hacker News Reader

Haven’t read the paper yet, but a great list of authors! I have worked on Knowledge Graphs at two companies, and I am fairly opinionated about them. I think that large fast KGs (i.e., no OWL reasoning) are a good tool for organizing different data sources, but I have lost my interest in KGs for Knowledge Representation (in the AI sense). Just a personal opinion, lots of people in the field would disagree with me.

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Mark would you say more about use of KGs for organizing data sources, or give an example situation? Thanks in advance.

Not Mark, but I'm one of the original leads on Google's Knowledge Graph.

KGs are great for data integration because they are naturally composable. If you have two data sources represented as graphs, and their node identifiers are in the same range (ie, they have been reconciled), and their edge labels are mapped to the same schema, then you can combine them just by taking the union of all their triples.

This works across different vertical domains -- for instance, you can create a joint film + music KG just by unioning all the triples from a film KG and a music KG, as long as you've mapped the node identifiers to a common namespace (eg Wikipedia). You can then do cross-vertical queries like soundtracks for Will Smith movies, etc.

Thanks for having made the SemWeb/LinkedData a reality for people.

>I have lost my interest in KGs for Knowledge Representation (in the AI sense)

Hi! Can you say more about that please?

What about embedding KGs? That could be useful?

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