31–40 of 76 posts
I wish all of you not to fall in the trap of ontologies. I worked very hard in this domain my conclusion is that all ontologies fail to scale eventually. I would recommend people in the field to go towards "perspectivism".
See also his talk https://www.youtube.com/watch?v=ujMgQqp8YSY
I wish all of you not to fall in the trap of ontologies. I worked very hard in this domain my conclusion is that all ontologies fail to scale eventually. I would recommend people in the field to go towards "perspectivism".
Knowledge graphs for text (the focus of the article) seem narrowly-scoped since they require "objective" facts and relations to be practical. Capturing the subjective and transient perspective of observations made by multiple observers (which is what we actually have access to) is more complicated. For example, asking the same person the same question may yield different answers based on their mood or other environme…
Earlier quoted context omitted.
Could you (please, pretty please) elaborate?
There's no such thing in practice as "California is in the US." What you can get in reality is "Alice told the computer in 2008 that Bob wrote in his 1999 book that California was in the US in 1470BC." Perspectivism is the understanding that it's impossible to interpret semantic "knowledge" without knowing the limitations and implicit context carried with the fallible, partial transcription of the truth (set in a wor…
Quantum mechanics actually says there's less information needed to describe X area of space than classical physics implies there is.
I've worked with systems where the relationships are typed and can have attributes just like the vertices allowing the system to model data in a more intuitive fashion.
SQL might be a good fit to model Knowledge Graphs, since FOREIGN KEYs can be named, using the CONSTRAINT constraint_name FOREIGN KEY … syntax. We thus have support to label edges. Nodes = Tables Edges = Foreign keys Edge labels = Foreign key constraint names
The one thing that's definite is that SQL is a bad choice for particular kinds of queries, though most graph databases don't seem to go much further than improving (?) the syntax a little bit and adding transitive closure (which is also present in several SQL databases). A few graph databases do allow for more complex (even arbitrary) inference, but this somehow never seems to make the headlines.
I wish all of you not to fall in the trap of ontologies. I worked very hard in this domain my conclusion is that all ontologies fail to scale eventually. I would recommend people in the field to go towards "perspectivism".