The thing about Beethoven's 9th and biological materials which is mentioned in the OP is just that, out of a very large knowledge graph, they found small subgraph isomorphic to a subgraph created from a text about the symphony. But they seem not to cover the fact that a sufficiently large graph with some high-level statistical properties would have small subgraphs isomorphic to a 'query' graph. Is this one good or meaningful in some way, or is it just an inevitable outcome of having produced such a large knowledge graph at the start? The reader can't really tell, because figure 8 which presents the two graphs has such a poor resolution that one cannot read any of the labels. We're just expected to see "oh the nodes and their degrees match so it has the right shape", but that doesn't really tell us that their system had any insight through this isomorphism-based mining process.
For the stuff about linking art (e.g. a Kandinsky painting) with material design ... they used an LLM to generate a description of a material for DALL-E where the prompt includes information about the painting, and then they show the resulting image and the painting. But there's no measure of what a "good" material description is, and there certainly is no evaluation of the contribution of the graph-based "reasoning". In particular an obvious comparison would be to "Describe this painting." -> "Construct a prompt for DALL-E to portray a material whose structure has properties informed by this description of a painting ..." -> render.
It really seems like the author threw a bunch of stuff against the wall and didn't even look particularly closely to see if it stuck.
Also, the only equation in the paper is the author giving the definition of cosine similarity, before 2 paragraphs justifying its use in constructing their graph. Like, who is the intended audience?
https://iopscience.iop.org/article/10.1088/2632-2153/ad7228#...