From Books to Knowledge Graphs

Re:From Books to Knowledge Graphs by jeromechoo on Tuesday March 14, 2023 @05:48PM (#35159379)

Not a paper, but NLP extraction into knowledge graph representations do exist and are in use today. For example, here's a general purpose NLP model that links organization and people entities (among others) to each other based on factual relationships described in the text — https://demo.nl.diffbot.com

This semantic extraction can be extrapolated to most any trainable context. A useful one I've worked with involved mapping supplier-partner relationships. A well built supply chain graph can identify every layer of risk in a single supply chain and provide the provenance to back it up.

IMO, the biggest blocker to more mainstream use of Knowledge Graphs (even in the commercial world) is an actually intuitive interface for knowledge exploration. The real market innovation behind GPT isn't its 175 billion parameters, its the prompt interface that makes ChatGPT so universally accessible.

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Re:From Books to Knowledge Graphs by dberenstein1957 on Tuesday March 14, 2023 @06:22PM (#35159901)

Lovely, for me coreference resolution also is a huge issue. I created this package (https://github.com/Pandora-Intelligence/crosslingual-corefer...) which is currently the only viable solution to do coref resolution in Dutch and some other low-resource language.

Also, if you want to scale, LLMs are going to prove to expensive, so eventually data need to be logged somewhere to create a fine-tuned model that can do sub-tasks and ideally do them better. What do you think?

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Re:From Books to Knowledge Graphs by jeromechoo on Wednesday March 15, 2023 @11:48AM (#35170130)

Nice! Yes, coreference resolution is surprisingly absent even in enterprise NLP.

Depends on what you mean by "better". With more accuracy?

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