most of these knowledge graph papers seems to be about linking shallow content, likes citations etc,
with all these strides in AI, GPT, etc, does anyone know of papers about extracting into graph or other form of knowledge representations deeper semantics ?
for example instead extracting what the paper is about, how it relates to other papers, nodes and edges linking to field specific terms/concepts mentioned, translation of paper specific terminology to commonly agreed terminology, why it's citing said references (is it an example of use of a technique ? a proof for a claim ? something that's being refuted, etc ..), where in the text it's being used. and other kinds of "deeper meaning".
I've glanced at some papers that seemed to do something remotely close to this but it was restricted to doing NER for only medical/biology terms.
with all these strides in AI, GPT, etc, does anyone know of papers about extracting into graph or other form of knowledge representations deeper semantics ?
for example instead extracting what the paper is about, how it relates to other papers, nodes and edges linking to field specific terms/concepts mentioned, translation of paper specific terminology to commonly agreed terminology, why it's citing said references (is it an example of use of a technique ? a proof for a claim ? something that's being refuted, etc ..), where in the text it's being used. and other kinds of "deeper meaning".
I've glanced at some papers that seemed to do something remotely close to this but it was restricted to doing NER for only medical/biology terms.