Statements and axioms. Merge two knowledge graphs, what can you reason about them?

IE people and obesity, people and mortality. Can you find correlations between the two?

The next layer up is to prove the trust in the data and your insights.

Also you are a decade late, google already bought freebase and made schema.org

Yes there was once a hype about these graphs and standards so called Resource Description Framework (RDF). It never took off. The knowledge itself requires hefty curation though, if it's in a field that requires low false positive rate such as pharmacology.

Not my area at all, just passing by and wondering to the extent and how they use knowledge graphs for drug discovery.

Some time back I had a peek at AstraZeneca's GitHub [0] and got me curious. I know in genomics they try to use custom hardware to accelerate the process using FPGAs and others [1].

Curious if anyone can shed light on knowledge graph use at scale is being accelerated.

[0] AstraZeneca; Awesome Drug Discovery Knowledge Graphs https://github.com/AstraZeneca/awesome-drug-discovery-knowle...

[1] Gene sequencing accelerates with custom hardware https://www.mewburn.com/news-insights/gene-sequencing-accele...

I only know in my area you can infer gene and protein interaction network using knowledge graphs to some degree. In drug discovery I've seen graphs of chemical knowledges and what can be drugged and what not. https://pubs.acs.org/doi/10.1021/acs.jpca.2c06408

There might be more publications on mining literature and building such graphs but I'm not following it much since deep learning took over.

"Training" appears once on the page, in the following paragraph:

Generative AI models such as large language models typically leverage huge amounts of unstructured data during their initial training and fine-tuning. Knowledge graphs can be leveraged to identify the structure of the underlying data which can then improve the amount of signal which is extracted from the data during the training.

Which is too vague to be of interest, generally uninformative, and furthermore to me reeks of LLM-generated babble instead of something a human wrote.

That point is also a bit of a stretch.

While there are a few papers on using KG/Ontologies to enhance training, this is really far from the mainstream, and i would be surprised if it would be used anywhere (outside of a research paper)