So many open source "Knowledge Graph"-y type projects concentrate on building them like databases, with a query language that assumes the data in them is correct. You see this in things like Freebase, DBPedia and Wikidata, where they typically end up in a triple store and you query using SPARQL.
This isn't how the real world works, and there isn't a lot of publicly available software that takes this into account. There aren't even than many papers about it (the Microsoft Probase paper is one, and there is work from Florida University(?) about using Markov chains to reason while taking probabilities into about).
So many open source "Knowledge Graph"-y type projects concentrate on building them like databases, with a query language that assumes the data in them is correct. You see this in things like Freebase, DBPedia and Wikidata, where they typically end up in a triple store and you query using SPARQL.
This isn't how the real world works, and there isn't a lot of publicly available software that takes this into account. There aren't even than many papers about it (the Microsoft Probase paper is one, and there is work from Florida University(?) about using Markov chains to reason while taking probabilities into about).
I'm excited to take a look at this.
[1] http://deepdive.stanford.edu/doc/general/inference.html