Enterprise agents must connect what organizational data means to the rules governing each action. Consider a life-insurance application where the insured and policy owner are different people: an underwriting agent must attribute evidence to the correct party and decide whether the coverage requires extra documentation or specialist approval. Retrieving a relevant passage is not enough; the agent must interpret its terms, scope, and conditions against the case facts.

This talk presents data ontologies and rule ontologies as complementary ways to make knowledge scattered across forms, schemas, procedures, and policy manuals explicit. Our data-ontology method uses language models to extract source-grounded schemas from blank forms, factor them into role-specific entities, attributes, and qualifiers, and align fields to canonical targets, leaving unresolved fields explicitly unmapped. Frozen equivalence judgments, source audits, and deterministic checks guide validation and repair.

For rules, a language model classifies procedure passages into source-linked obligations and generates candidate Rego predicates against a version-pinned schema and connector contracts. Deterministic checks validate references and bindings, then drive a single repair pass. Each rule keeps its version, citations, and assumptions, with explicit gaps where human judgment is required.

Connecting the two is harder than it looks: a rule can pass every structural check yet reference the wrong party's consent. Through insurance servicing use cases, we trace forms and procedures end to end, then discuss how to evaluate and maintain these representations without mistaking structural validity for semantic correctness.

Speaker: George Karypis
Chief Scientist, NTT DATA AIVista
Distinguished McKnight University Professor
Department of Computer Science and Engineering, University of Minnesota