Back-translation from the graph language (Craig Layer 3, Unit 6) #
The translation back into L: G_f(x⃗, y) ↦ f(x⃗) = y, base relation atoms recover the
original relations, and — separately — native equality stays equality after term erasure
(a relational interpolant may introduce equality even when no G_f occurs, and its
back-translation then correctly uses no function symbol).
Everything here holds for every L-structure over its graph expansion — no symbol set,
graph axioms, choice, or reconstruction enters.
Main Results #
realize_backTranslateFormula— over the graph expansion, the back-translation realizes exactly as the graph-language formula.functionsIn_backTranslateFormula/relationsIn_backTranslateFormula— the exact occurrence identities in preimage form: the back-translation's function symbols aregraphRelSym L ⁻¹' θ.relationsIn, its relation symbolsbaseRelSym L ⁻¹' θ.relationsIn.functionsIn_backTranslate_subset/relationsIn_backTranslate_subset— the two consumer lemmas Unit 7 wants: an interpolant withrelationsIn ⊆ relSym L F Rback-translates with function symbols inFand relation symbols inR(via the embeddings' injectivity and cross-disjointness; no Sigma bookkeeping leaks to the Craig endpoint).realize_backTranslate_relationalize— acceptance corollary: back-translating a relationalization is semantically the original formula (no syntactic identity claimed).
Term erasure #
Erase a graph-language term to an L-term: the graph language has no function symbols, so
this is variable-preserving with the function case impossible.
Equations
Instances For
The back-translation #
Back-translate a graph-language formula to L: native equality stays equality (after term
erasure), a base atom recovers the original relation, a graph atom G_f(x⃗, y) becomes
f(x⃗) = y, and all connectives and quantifiers are structural.
Equations
- One or more equations did not get rendered due to their size.
- FirstOrder.Language.backTranslateFormula FirstOrder.Language.BoundedFormulaω.falsum = FirstOrder.Language.BoundedFormulaω.falsum
- FirstOrder.Language.backTranslateFormula (φ.imp ψ) = (FirstOrder.Language.backTranslateFormula φ).imp (FirstOrder.Language.backTranslateFormula ψ)
- FirstOrder.Language.backTranslateFormula φ.all = (FirstOrder.Language.backTranslateFormula φ).all
- FirstOrder.Language.backTranslateFormula (FirstOrder.Language.BoundedFormulaω.iSup φs) = FirstOrder.Language.BoundedFormulaω.iSup fun (i : ℕ) => FirstOrder.Language.backTranslateFormula (φs i)
- FirstOrder.Language.backTranslateFormula (FirstOrder.Language.BoundedFormulaω.iInf φs) = FirstOrder.Language.BoundedFormulaω.iInf fun (i : ℕ) => FirstOrder.Language.backTranslateFormula (φs i)
Instances For
Semantics over the graph expansion #
The back-translation semantics: over the graph expansion of any L-structure, the
back-translation realizes exactly as the graph-language formula. No symbol set, graph axioms,
choice, or reconstruction.
Acceptance corollary: back-translating a relationalization is semantically the original
formula, in every L-structure (no syntactic identity claimed).
The exact occurrence identities, in preimage form #
The back-translation's function symbols are exactly the graphRelSym-preimage of the
formula's relation symbols (native equality contributes nothing — term erasure is
function-free).
The back-translation's relation symbols are exactly the baseRelSym-preimage of the
formula's relation symbols.
The consumer lemmas for the Craig assembly (Unit 7) #
An interpolant whose relation symbols lie in relSym L F R back-translates with function
symbols in F.
An interpolant whose relation symbols lie in relSym L F R back-translates with relation
symbols in R.