This suggestion revisits the classic "formal top-down" vs "informal bottom-up" approaches to building a semantic knowledge management system. Top-down has been tried extensively in the pre-big-data models and pre-probabilistic models era, but required extensive manual human curation while being starved for knowledge. The rise of big-data bode no cure for the curation problem. Because its curation can't be automated, larger scale just made the problem worse. AI's transition to probability (in the ~1990s) paved the way to the associative probabilistic models in vogue today, and there's no sign that a more-curated more-formal approach has any hope of outcompeting them.
How to extend LLMs to add mechanisms for reasoning, causality, etc (Type 2 thinking)? However that will eventually be done, the implementation must continue to be probabilistic, informal, and bottom-up. Manual human curation of logical and semantic relations into knowledge models has proven itself _not_ to be sufficiently scalable or anti-brittle to do what's needed.
How to extend LLMs to add mechanisms for reasoning, causality, etc (Type 2 thinking)? However that will eventually be done, the implementation must continue to be probabilistic, informal, and bottom-up. Manual human curation of logical and semantic relations into knowledge models has proven itself _not_ to be sufficiently scalable or anti-brittle to do what's needed.