Understanding English promotes better policies and requirements

Capturing some policies from a publication by the Health and Human Services department recently turned up the following….
It’s probably the case that there are more specific lists than just “some list” or “any list”, as suggested below.
This is a good thing about applying deep natural language understanding to policy statements.  It helps you say precisely what you mean, even if you are not using a rule or logic engine, but just trying to articulate your policies or requirements clearly and precisely.

HHS on expedited review

Logic from the English of Science, Government, and Business

Our software is translating even long and complicated sentences from regulations to textbooks into formal logic (i.e,, not necessarily first-order logic, but more general predicate calculus).   As you can see below, we can translate this understanding into various logical formalisms including defeasible first-order logic, which we are applying in Vulcan’s Project Halo.  This includes classical first-order logic and related standards such as RIF or SBVR, as well as building or extending an ontology or description logic (e.g., OWL-DL).

We’re excited about these capabilities in various applications, such as in advancing science and education at Vulcan and formally understanding, analyzing and automating policy and regulations in enterprises.

English translated into predicate calculus

English translated into predicate calculus

a sentence understood by Automata

an unambiguously, formally understood sentence


English translated into SILK and Prolog

English translated into SILK and Prolog