Maybe for its time it seemed like a good idea.. Like SOAP or manual features for image classification. Today, it's clear that languages and knowledge don't really work like that, and it's not practical to approach them this way. I've learned about the OWL and SPARQL 12 years ago, and it already felt like a very dated idea. But then who knows... Everybody have given up on NNs once too.
I would prefer to have more data (e.g. data from US federal reserve data, world bank data) as RDF and accessible via SPARQL endpoints than less, because it is much more useful as RDF than as CSV, in my opinion.
The comparisons to NLP presents a good view on the problems.
Its "easy" to write some logic rules to parse input text for a 50% demo. But then you want to improve & scale, and suddenly all the nuances, bites you. The rules get bigger, nested and complicated. Traditional NLP tried that avenue for a while, with decent success in small usecases, but for larger problems without success. (Compared to stuff like BERT & GPT, which still have a lot of problems)
Similar with Knowledge Graphs, you can show some nice properties on inferring knowledge on small problems, but the real world is much more approximate and unclear than some (binary) relationships.
Personally i think we Humans lack the mental capacity to build large models with complex interactions.
That's true as individuals I do not believe we can. Only as a group and with the help of tools, which is what semweb tried to achieve. We found out the tools weren't the most practical and learned a lot. Now we need the Tensorflow of those approaches, something easy to use not platform centric and with a low barrier of entry.
OWL tools are dated. One large lib OWLAPI that is full of bugs and impractical, available only for one platform (JVM). Reasoners that work or not, and get abandonned once the grant that funded them is finished... Reasoners that use OWLAPI x but never got ported to OWLAPI y so no way to use it on more recent systems...
