Extracting triplets is useful for explain-ability. You know where you took the fact from, so it help build a consistent view of the world. In fact this consiste

Extracting triplets is useful for explain-ability. You know where you took the fact from, so it help build a consistent view of the world. In fact this consistency can be used as a signal for training your algorithms in an unsupervised way. It is useful for building a search engine because it helps create relevant index automatically. Once you have a way to construct the knowledge graph automatically, you can made better recommendations. One usually good recommendation is inferring the graph query which gave some results the user was interested in. Then you can run the query and propose to the user an exhaustive set of similar results. Basically it's a great way to intuit the intent of the user : It helps you get the "Why". An other application, which is a little extension is to use quad-stores instead of triplets, so you can have facts about the facts. It is very useful to highlight the fake news/fake users. Or to identify the point of view of various individuals for further targeting. The more promising ventures of this type of work is that Graph Query Language are kind of an easy programming language and are a good stepping stone toward more useful programming languages. This is probably one step on the path toward program induction.