Network Effects | suss suspect device
Studia Humanitatis – παιδεία – ΟYΤΩΣ AΤΑΛΑIΠΩΡΟΣ ΤΟIΣ ΠΟΛΛΟIΣ H ΖHΤΗΣΙΣ ΤHΣ AΛΗΘΕIΑΣ, ΚΑI EΠI ΤA EΤΟIΜΑ ΜAΛΛΟΝ ΤΡEΠΟΝΤΑΙ. «Così
Dr. Shawn M. Graham
Description: Human Cogitation, Human Explanation
( cross posted from the XLab ) So far in our experiments with knowledge graphs and large language models (see here, and here ) we’ve been content to work with the unstructured text and let the data model, such as it is, to percolate up from below, trying to impose a bit of structure on it afterwards by seeing what subject – predicate – object triplets emerge, and working with the most common predicates.
No doubt this is not the best way to go about it. Today we came across this post by Peter Lawrence where he used an RDF ontology in ttl format as part of his GPT-3 prompt against unstructured text to see if that would make his extracted knowledge graph more rigorous. He found that it did; so we thought we’d give it a try too. You can see the transcript of his chat here.
Our problem of course is that we do not have a well-specified ontology. But we figured, as part of its training, that chat gpt has probably seen – let’s be frank – far more examples that any of us have ever encountered. So we fired up chatgpt to see what we could see.
Network Effects | suss suspect device
Studia Humanitatis – παιδεία – ΟYΤΩΣ AΤΑΛΑIΠΩΡΟΣ ΤΟIΣ ΠΟΛΛΟIΣ H ΖHΤΗΣΙΣ ΤHΣ AΛΗΘΕIΑΣ, ΚΑI EΠI ΤA EΤΟIΜΑ ΜAΛΛΟΝ ΤΡEΠΟΝΤΑΙ. «Così
Dr. Shawn M. Graham