I Know Why The Semantic Web Failed

Proposed: “The Non-Oxford/Non-Webster Dictionary, Patrick Durusau, Editor, shall invent new means of identifying the meaning of previous words and a means for processing them.” Despite computer science precedence this approach, no known dictionary has ever used it. Much like the “Attention is all you Need” linguists, dictionaries rely on existing data as a starting point. As a practical matter, as well as human nature, users prefer names they already know for subjects; witness the persistence non-standard names or terminology, long after new lists have been invented. Character sets and Unicode points are one example. The Semantic Web took that a step further and invited users to create, at their own expense, identifiers for subjects they knew by other identifiers. An origin paper sums up the problem we face with identifiers this way: It turns out that given any term, there are many possible subjects that it could denote (to a greater or lesser extent) and conversely, any particular subject of knowledge (whether broad or narrow) usually can be denoted by different terms. What if instead of adding to the sea of identifiers for subjects, we take inspiration from probabilistic database and large language models to develop a data-driven approach for subject identity? Instead of a universal exactness of subject identity, the degree of certainty or rather uncertainty, is acknowledged as a matter of design.