Posterior Ontology Memory: Bayesian Prediction Across Mutable Semantic Schemas

Abstract

Posterior Ontology Memory (POM) models uncertainty in the vocabulary used by a symbolic memory, rather than assuming that its semantic schema is fixed. It treats mappings from surface predicates to latent semantic categories as uncertain and carries that uncertainty into prediction. The model combines a partition prior, collapsed Beta–Bernoulli rule models, noisy compiler judgments, and Bayesian model averaging. A temporal version allows schemas to split or merge and rule rates to reset, using exact enumeration for small reference cases and sequential Monte Carlo for approximate inference.In finite grounded-rule experiments, model averaging improves held-out prediction when surface predicates share latent behavior, but the advantage disappears when that assumption breaks down. The temporal study passed seven of eight predeclared gates: delayed split evidence met its criterion, while delayed merge evidence did not. In a separate study of 19 GitHub API migrations, a local language model retrieved 18 correct notices, compared with 7 for token overlap, although direct mutation classification was less reliable. The paper presents a bounded proof of concept for symbolic memory that retains uncertainty over changing semantic schemas.