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Comment by dexter89_kp3 | Hacker News Reader

Any links/resources to the opposite problem? i.e generating accurate knowledge graphs from corpora of documents?

Google does have an experimental API, but have not found an associated blog post or paper with it: https://cloud.google.com/ai-workshop/experiments/generating-...

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The field of Open Information Extraction has been trying to do that in a generic way for a long time, but the results are still far from good. A few references: OpenIE [1] Graphene [2] MinIE [3].

If you already have a Knowledge Graph (KG) and want to populate its instances from documents, that's called KG Population, and Knowledge-net [4] is a good reference.

Relation Extraction is another interesting approach if you know which kind of relations you're interested in, OpenNRE [5] a good example.

[1] https://github.com/dair-iitd/OpenIE-standalone

[2] https://github.com/Lambda-3/Graphene

[3] https://github.com/uma-pi1/minie

[4] https://github.com/diffbot/knowledge-net

[5] https://github.com/thunlp/OpenNRE

I'm hip deep into this subject myself. I tried modding TiddlyMap and just now am checking out InfraNodus. When you dig into this, you find there's not a standard method because natural language is itself deeply non-standardized. To take one example, a procedure uses the same structure as a ordered list, but a procedure regards the sequence as representing a temporal structure, whereas the ordered list is just using sequence as providing a unique identifier - it doesn't even need numbers or precise intervals. You need NLP to chip out the subject-noun-verb from the ordered list items, or you need an element or role telling you what you are looking at.

If someone way more smart than myself could chip in on the subject, that would be pretty dang awesome.

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