Discussion topic - A lot of conversation has gone into what KGs can do, but what can't they do? Are there antipatterns for KGs that commonly occur?
GREAT question. Ashleigh F. does emphasize the importance of determining if kg is the right fit for a job...but this is not exactly the same question. however, perhaps she has a opinion?
also seems like something Paco N. could speak to, and Kurt Cagle
Anti-patterns are among my favorite topics 馃檪 There are good indications for using KGs: e.g., having linked data, having suitable ontologies/controlled vocabularies which are well-defined, etc.,, and of course one could also create or extend vocabularies as needed. KGs are great for representing relationships; in contrast RDBMS have relatively static definitions of relationships. The intended use case is also importance to consider: we understand about uses for embedding, link prediction, entity linking, neighborhood search, disambiguation, and we could go on with articulating others ... but if the intended use case doesn't fit, then some research/invention may be required. That can be difficult, expensive, unlikely, etc.
Probably the conversation that I hear repeated most about KGs is where they fit into "AI" in general ... probably followed by investor-ish inquiries about "What do KGs replace?" My favorite one-liner about that is that KGs represent context, and context is the thing that ML typically removes when generalizing from patterns in data. That's where the industry is now with "How can we determine why this ML model predicted no loan for one person but not another?" because the context is difficult to trace in ML models when you're required to describe both the local and global interpretation. So KGs are coming up at a very helpful time, to help restore some of the context, and we see that in use cases that leverage KGs for explainability.
wow, Paco, thank you for the tremendous insights! did that answer your question, Jessica K.?