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Reuse: Surveying and Aligning Existing Ontologies

📍 Where we are: Part II · Reuse — the second phase of the lifecycle, governed by the NeOn reuse scenario and the reuse-first authoring discipline of LOT. The specification is written and the running example is loadable. Before we draw a single new class, we go shopping: we survey what the world already publishes, decide what to borrow, and write the borrowing down as one file.

The cheapest class in an ontology is the one you never had to author. The specification fixed what the model must answer; the reuse phase fixes how much of the answer already exists. For a CHO monoclonal-antibody campaign the answer is: almost all of it. The Chinese hamster the line is built from is a node on the public tree of life; the IgG complex the cells secrete, the Protein A capture step that pulls it out of the broth, the chromatography medium that step runs on, and the "process parameter" a feed rate is an instance of are each a published class somewhere with a stable IRI and a community maintaining it. Reuse-first is not frugality for its own sake — it is the only way a private campaign graph becomes interoperable with the rest of biomedicine and manufacturing the moment it is loaded, rather than after a retrofit nobody budgets for [4].

The simple version

Before building a kitchen, you do not forge your own screws. You buy standard screws, standard pipe fittings, a standard electrical socket — because the dishwasher you install next year expects exactly those, and a homemade socket means a homemade dishwasher forever. Reusing an ontology is buying standard parts: the screw is NCBITaxon for "Chinese hamster," the pipe fitting is iof:CaptureStep for "Protein A capture." This chapter is the trip to the hardware store — surveying the aisles, choosing the parts, and writing the parts list — done before any custom welding begins.

Start from the questions​

Reuse is the book's one non-functional requirement made operational: the ORSD lists reuse-first — "align to public ontologies, mint only what is genuinely local" — as a quality the model must have regardless of any single competency question. But it also directly underwrites one functional question. CQ-20 asks what host organism, by stable NCBI Taxon IRI, does the line express its product in? — and the only honest answer for a CHO line is Cricetulus griseus, named not by the ambiguous three-letter string "CHO" but by obo:NCBITaxon_10029. That question is unanswerable unless the model has already reused NCBI Taxonomy: you cannot return a stable taxon IRI you never borrowed. And the question matters because the host is the deepest fact in the genealogy — the cell line is alive, mutates, and drifts, and naming its species by a stable identifier shared with the public databases is the first defence against a misidentified root propagating, with full confidence, to every batch beneath it. Reuse is therefore not a stylistic preference; it is a precondition for a specific safety-critical test passing, which is exactly why the lifecycle puts it before conceptualization rather than treating it as polish.

Conceptualize the reuse: a layered survey, grounded in one batch​

Ontologies stack, and they stack for a manufacturing reason: a single batch of antibody is not one kind of thing but several, and each kind is best described by a different layer. Take BATCH-2026-001. The batch material — the cells, the broth, and later the purified protein — is one thing; the production bioreactor that held it is a different thing, because equipment persists across many batches and must not be fused into the material; the fermentation that grew the cells is a third thing that both participate in; the 98.611 % monomer purity is a fourth, a quality that inheres in the drug-substance lot and nowhere else; the vessel's standing as "this campaign's production reactor" is a fifth, a role it bears and could shed; and the master batch record that prescribed the whole run is a sixth, copyable information distinct from any printout. Six entities, one batch — and the NeOn reuse scenario tells us to look for reusable resources at every level that family of entities spans, because borrowing high up buys the most compatibility per import. Surveying the campaign top-down yields four layers.

  • Foundational. One upper ontology grounds everything: BFO, the Basic Formal Ontology, standardized as ISO/IEC 21838-2 [1]. It contributes no domain terms — only material entity, quality, process, role — but it is precisely what lets the six things in that one batch each find the right kind of home. The vessel and the broth are both material entities (obo:BFO_0000040) yet stay separate individuals; the fermentation is a process (obo:BFO_0000015) the model never confuses with the material it outputs; the purity is a quality (obo:BFO_0000019) that cannot be accidentally asserted of the empty tank; the production-reactor standing is a role (obo:BFO_0000023). This continuant/occurrent split — things that persist versus things that happen — is the spine the upper-spine chapter built, and it is what buys batch traceability: because the vessel and the material are distinct continuants, the graph can later say a second batch ran in the same BR-101 without contradiction; because the fermentation is an occurrent both participate in, "when did this happen?" has somewhere to attach.
  • Mid-level. The IOF Core, published by the Industrial Ontologies Foundry, sits between BFO and the plant: material artifact, manufacturing process, piece of equipment, requirement specification [5]. This is the rung that makes a bp:Batch an IOF material artifact and a fermentation an IOF manufacturing process — the generic manufacturing vocabulary a 98.611 %-purity lot needs before any antibody-specific term is drawn. IOF was modeled deliberately on the OBO Foundry, the life-sciences community that proved coordinated, principle-based ontology building works at scale [3].
  • Domain. Two domain families meet here. The IOF biopharma modules supply the manufacturing interior the campaign runs through: bioreactor, chromatography column and medium, membrane filter, and the unit-operation chain itself — capture, viral-clearance, polishing, UF/DF, and formulation — plus the cell line and cloned cell line, and the QbD parameter/range classes that link a feed rate to a purity. The OBO members supply the biology and the science under those operations: the Gene Ontology (GO) for the IgG molecular function [6], the Protein Ontology (PRO) for the antibody's target [7], NCBI Taxonomy for the Chinese-hamster host [9], the Cell Line Ontology (CLO) for the CHO line [10], a Human Disease Ontology for the indication the product concept names [8], OBI for assays and investigations [11], ChEBI for the formulation excipients, NCIt for the product and mechanism-of-action concepts, and IAO for information artifacts such as the master batch record.
  • Cross-cutting. Orthogonal vocabularies that any domain reuses: QUDT for units (so the purity, the pH of a viral-inactivation hold, and a fill volume never travel as bare numbers), PROV-O for activity provenance, SOSA for the bioreactor sensors, SKOS for definitions and mappings, Dublin Core for metadata, and the Allotrope Foundation Ontologies (AFO) for the chromatography devices and the analytical results that decide release.
# align.ttl — the four reuse layers, each a real prefix bound to a verified host.
@prefix obo:   <http://purl.obolibrary.org/obo/> .                       # BFO, RO, OBI, GO, CLO, NCIT, NCBITaxon, PR, DOID, IAO, PATO
@prefix iof:   <https://spec.industrialontologies.org/ontology/construct/> . # IOF Core (classes mint under /construct/)
@prefix af-p:  <http://purl.allotrope.org/ontologies/process#> .
@prefix qudt:  <http://qudt.org/schema/qudt/> .
@prefix prov:  <http://www.w3.org/ns/prov#> .
@prefix sosa:  <http://www.w3.org/ns/sosa/> .
@prefix dct:   <http://purl.org/dc/terms/> .

That single prefix block is the survey's result: every external world the model touches, named once, before a domain class is drawn — and each prefix earns its place because one of the six entities inside a real batch needed it.

How candidates are found: the registries, and what they cannot tell you​

A term you cannot locate you cannot reuse, so the survey is a search across the field's registries. Each ontology in the layers above was found, not guessed, and the finding leaves a trail of where to look:

  • OLS4 — the EBI Ontology Lookup Service — is the front door for the OBO world. Every BFO, RO, OBI, GO, CLO, NCIT, NCBITaxon, PR, DOID, IAO, and PATO IRI in this model was confirmed there, against the live term page — including the obo:NCBITaxon_10029 that names the Chinese hamster and the obo:CLO_0002421 "CHO cell" that names the line.
  • The published IOF release is the front door for IOF, because OLS does not host IOF. The IOF Core and biopharma terms — the bioreactor, the capture step, the chromatography medium, the drug-product formulation process — were verified against github.com/iofoundry/ontology at Release_202602; both the term IRIs and the module ontology IRIs are live and dereferenceable.
  • BioPortal and OBO Foundry round out the OBO search — OBO Foundry's principles page tells you which ontology is the canonical home for a given kind of thing (the target protein → PRO, the indication → a disease ontology) so two antibody programs reach for the same one [3].
  • LOV (Linked Open Vocabularies) and the publishers' own sites cover the cross-cutting layer: QUDT at qudt.org, PROV-O and SOSA at the W3C, AFO at the Allotrope release.

What the registries cannot tell you is the domain knowledge needed to pick the right hit — and that barrier is real in biopharma. A registry will return dozens of "cell line" terms; only someone who knows that a manufacturing cell line is a managed production input with a research-master-working banking discipline will know that the manufacturing sense lives in IOF biopharma while the biological sense (a CHO cell, a node in the cell-line tree) lives in CLO. A registry lists "chromatography medium" without knowing that for a Protein A capture step the medium is the consumable whose cycle count and carryover a graph may later need to trace. The honest record of where each IRI was checked is itself a reuse artifact — written into the header of the alignment file — because a borrowed term whose source you cannot name is a term you cannot defend in a review, and a regulator reviewing a CHO process will ask exactly that.

Select what to borrow: the criteria, and why a near-miss is dangerous​

Finding a candidate is not the same as adopting it. Each survey hit is judged against a fixed set of selection criteria before it earns an edge — and in a regulated antibody campaign the cost of a wrong adoption is not abstract:

  • Coverage — does the term mean exactly the local thing, or only roughly? A near-miss is worse than a local mint, because it asserts a falsehood that travels. (This is why bp:Specification aligns to iof:RequirementSpecification — there is no bare iof:Specification class to borrow.)
  • Maturity — is the term Released, or provisional and likely to move? A re-audit of the IOF biopharma release found its bioreactor, chromatography-column, and master-recipe classes were all Released, not the Provisional an earlier pass had assumed — which is what let the campaign's equipment and recipe be adopted with confidence rather than left as flagged local mints.
  • Adoption — is the ontology actually used by the databases and partners you want to interoperate with? GO and PRO win on this alone; public databases annotate to them by default, so the antibody's target already has a PRO IRI and a GO molecular-function annotation waiting [6][7].
  • Logical compatibility — does it sit on the same BFO spine, so importing it cannot make the model inconsistent? A CLO cell type and an IOF cell line both descend from BFO, which is the only reason one bp:CellLine can wear both at once.
  • License and import cost — is it freely reusable, and how heavy is the file you would pull in?

When a candidate fails coverage and no faithful 1:1 external term exists, the discipline is to keep the class local and flagged, not to fake a borrow — and the biggest such gap in this campaign is at fill-finish. The IOF biopharma release has a drug product formulation process but no fill-finish, aseptic-fill, or lyophilization class at all, so bp:FillFinishProcess, bp:ContainerClosureSystem, and bp:fillsInto stay flagged local classes. The temptation is to bend some near-term onto them; the cost of doing so is concrete: a downstream importer assembling a regulatory submission expects the real meaning of "aseptic fill," and a faked edge would silently mislabel the single step where the bulk antibody becomes the sterile, sealed vials a patient receives. Charge variants, aggregate variants, GS1 packaging tiers, and the governance records are kept local for the same reason — a documented gap is honest, an unfaithful edge is a latent error that surfaces at the worst time.

Formalize the borrowing: align.ttl, and the import gap​

The reuse phase produces exactly one artifact: align.ttl, a file that does nothing but assert rdfs:subClassOf (and rdfs:subPropertyOf) edges from each local bp: term up to a real, dereferenceable external IRI. Here is the biology-and-manufacturing core of it — the host borrowed from NCBI Taxonomy, the line's biology from CLO, its manufacturing role from IOF biopharma, the IgG itself from GO, and the capture consumable from IOF:

# align.ttl — the host is borrowed (NCBI Taxonomy), the line's biology (CLO) and role (IOF) too.
bp:HostOrganism        rdfs:subClassOf obo:NCBITaxon_10029 . # NCBI Taxonomy 'Cricetulus griseus' (Chinese hamster, verified via OLS4)
bp:WorkingCellBank     rdfs:subClassOf obo:CLO_0002421 .   # CLO 'CHO cell' (Chinese hamster ovary cell line)
bp:CellLine            rdfs:subClassOf iof:CellLine .          # IOF biopharma 'cell line' (Released, Release_202602)
bp:Clone               rdfs:subClassOf iof:ClonedCellLine .    # IOF biopharma 'cloned cell line'
bp:Antibody            rdfs:subClassOf obo:GO_0071735 .   # GO 'IgG immunoglobulin complex'
bp:Excipient           rdfs:subClassOf obo:CHEBI_24431 .   # ChEBI 'chemical entity' — the generic anchor; specific excipients (polysorbate, histidine, sucrose) would use ChEBI leaves
bp:CaptureChromatography rdfs:subClassOf iof:CaptureStep .                 # IOF 'capture step' (Protein A capture)
bp:ChromatographyResin rdfs:subClassOf iof:ChromatographyMedium .    # IOF biopharma 'chromatography medium' (Released)

Each line carries a manufacturing fact, not just a vocabulary edge. bp:HostOrganism rdfs:subClassOf obo:NCBITaxon_10029 is what lets the cell-bank root name its species by a stable identifier instead of a string — the genealogy's first line of defence against misidentification. bp:Excipient rdfs:subClassOf obo:CHEBI_24431 anchors the polysorbate-80, histidine, and sucrose that keep the formulated antibody stable to ChEBI's generic chemical entity; the dataset deliberately stops at the generic anchor and flags the specific leaf IRIs as illustrative, exactly the honest-gap discipline above. And bp:CaptureChromatography rdfs:subClassOf iof:CaptureStep types the Protein A step every batch passes through as a shared manufacturing concept a partner's tool already understands.

Now the most important sentence in the chapter, because it is the gap most adoptions trip on. align.ttl contains no owl:imports. Asserting rdfs:subClassOf iof:CaptureStep gives a real, shared IRI another team's IOF-aware tool can line up with — but on its own it does not make a reasoner conclude that a bp:CaptureChromatography is a BFO process, because the chain from iof:CaptureStep up through IOF to BFO lives inside files the validator does not load. The validator loads only bioproc.ttl + align.ttl + instances.ttl, so the OWL-RL closure gets transitive rdfs:subClassOf over the edges asserted here, plus the local axioms — but not IOF's own chain and not BFO's disjointness. The result is lexical interoperability (shared, real, dereferenceable IRIs), not cross-ontology entailment.

What does that buy in the antibody world, concretely? A contract manufacturer or a regulator whose tools speak IOF can line up your bp:CaptureChromatography batches with their notion of a capture step without ever comparing English prose; a public database that annotates proteins to PRO can join your antibody's target to its records; a partner querying "which lots came from a Chinese-hamster line?" gets your batches back because the host carries the same obo:NCBITaxon_10029 everyone else uses. If they could not align — if the host were the string "CHO" and the capture step a private code — every one of those joins becomes a hand-built translation table maintained forever. To turn the assertion into an inference you owl:imports IOF Core (which itself imports BFO 2020), and the reasoner then classifies your terms through the whole stack. The running example does both halves honestly: align.ttl reuses the IOF terms it could confirm, and a companion bioproc-imports.ttl carries the real owl:imports declarations — while the offline validator stays scoped to what it can prove without fetching the external stack. The one-line summary: alignment buys shared vocabulary for free; cross-ontology reasoning only once you import.

A four-rung layered stack narrowing downward: a neutral upper ontology BFO at the top; an IOF Core mid-level of generic manufacturing concepts below it with a side note that IOF was modeled on the OBO Foundry; the IOF biopharma domain ontologies plus OBO members bioreactor, chromatography column, cell line, master recipe, GO, PRO, NCBI Taxonomy, CLO below that; and a local bp namespace at the bottom that aligns up rather than inventing new classes, with a straight arrow showing every rung specializing the one above and one knowledge graph at the base fed by all four rungs. The reuse stack the survey shops: a neutral upper ontology, an industrial mid-level modeled on the OBO Foundry, the biopharma domain plus the OBO members for the biology, and the local vocabulary that aligns up to all of them — so a local class inherits compatibility for free, and align.ttl is the file that asserts every one of those upward edges. Original diagram by the authors, created with AI assistance.

The OBO–IOF seam: a crosswalk you author, running through a living cell​

Reuse here is not a single clean import; it straddles a fault line, and the fault line runs straight through the most important node in the whole graph — the cell line. The ontologies that best describe a cell line's biology — CLO, NCBI Taxonomy, GO, PRO — grew up in the OBO Foundry. The ontologies that best describe making the antibody — IOF Core and IOF biopharma — grew up in the Industrial Ontologies Foundry. Both descend from BFO, which is precisely why a single bp:CellLine can sit under both obo:CLO_0002421 (its biology, the CHO cell) and iof:CellLine (its manufacturing role, a managed production input) at once. But "can meet" is not "have met": there is no published bridge that says how CLO's CHO cell relates to IOF's cell line, so that crosswalk is author-curated — a mapping the program writes, reviews, and maintains, not a feature it imports.

The seam matters here more than anywhere because of why cell banks need genealogy in the first place. A cell line is alive: a population of cells that mutate and drift over generations, so the working bank is not genetically identical to the master, and a culture grown too long can lose productivity or shift its product quality. That is why the campaign keeps a research / master / working banking hierarchy (RCB → MCB → WCB) — a structured taxonomy of disjoint tiers, characterized and passage-limited — rather than one vial nobody can vouch for. The biological side of the seam (is this still a CHO cell? what is its species?) and the manufacturing side (is this WCB within its validated passage limit and fully characterized?) are both true of the same WCB-CHO-001, and only an authored crosswalk holds them together. The target chapter names this seam at the day-one discovery boundary, where the antibody is still a product concept — an information artifact that exists before any cell makes the molecule; the reuse phase is where the seam becomes a maintained artifact, because every borrowed edge that crosses it is one the author justified term by term.

Evaluation: does the reuse hold up?​

Reuse is validated the same way every phase is — by loading the graph, reasoning over it, and running the competency question that depends on it. CQ-20 is that test, and it is a pure reuse check: it asks the reasoned graph to return the host organism's stable external taxon IRI, which exists only because bp:HostOrganism was aligned to obo:NCBITaxon_10029:

# queries/CQ-20.rq — CQ-20: the host is named by a stable public identifier, not the string "CHO".
PREFIX bp:   <https://example.org/bioproc#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX obo:  <http://purl.obolibrary.org/obo/>
SELECT ?host ?taxon WHERE {
  bp:WCB-CHO-001 bp:hasHostOrganism ?host .
  ?host a ?cls .
  ?cls rdfs:subClassOf ?taxon .
  FILTER(STRSTARTS(STR(?taxon), STR(obo:)))
}

The check in cq-catalog.json requires the single row host = CHO-host, taxon = NCBITaxon_10029 — and validate.py returns exactly that. Note the query starts at bp:WCB-CHO-001, the working cell bank at the root of every genealogy chain, and traverses an edge that lives in align.ttl (bp:HostOrganism rdfs:subClassOf obo:NCBITaxon_10029): the test passes only because the reuse was done, and would fail the instant the host were typed by the bare string "CHO" instead. A second reuse-dependent test runs further downstream: because bp:derivedFrom was aligned to the OBO Relation Ontology's obo:RO_0001000 derives from and declared transitive, CQ-01 walks DP-001's lineage back through the purification chain — drug substance, polishing, viral-filtration, viral-inactivation, capture pool, clarification, the bioreactor batch, the seed train, and the three cell-bank tiers — to 11 ancestors, with no join table and no hand assertion. Across the full dataset the alignment scales to a graph of 2120 triples closing to 7137 under OWL-RL, with the distinct IOF classes consumed grown to 27 (7 Core + 20 biopharma) — the measurable yield of shopping before building.

The unsolved part: a borrow can drift, and a near-miss can hide​

Reuse trades one risk for another. By aligning to external IRIs the model inherits their meaning — but also their changes. NCBI Taxonomy revises, IOF ships new releases, a CLO term can be obsoleted; an edge verified against Release_202602 is true at that release and is a maintenance obligation thereafter, which is why the governance chapter treats the alignment as versioned content under change control. The subtler hazard is the near-miss that looks faithful: NCIt's Indication and Mechanism of Action are bound as lexical bridges to a shared concept name, not as BFO category claims, because NCIt is a thesaurus, not a BFO-partitioned ontology — assert them as if they were entailment-grade and you have smuggled an unfaithful claim into the graph. There is a deeper limit the alignment cannot touch at all: the cell line it so carefully names is alive. An IRI implies a crisp, stable identity, but no owl:sameAs and no borrowed CLO term answers whether the culture at passage 60 is the same entity as the culture at passage 5, and no alignment can certify that the vial labelled WCB-CHO-001 actually contains the line the label claims. Cell lines have been confused and cross-contaminated across the life sciences for decades — which is exactly why stable identifiers and authentication exist [10] — and for a manufacturing root node a misidentification is the worst possible error: asserted with full confidence, it propagates transitively through derivedFrom to every descendant batch, and no amount of downstream data integrity catches it, because every downstream fact is correctly derived from a wrongly identified root. Reuse gives the root a stable, public name; it cannot make the thing at the root be what the name says. The discipline that keeps reuse honest is the same one that makes it valuable: borrow only what genuinely matches, name the source, and flag — never fake — the gap.

Why it matters​

The reuse phase is where a model decides whether it will be interoperable or merely internally consistent. Borrow the host as obo:NCBITaxon_10029 and a partner can ask "which lots came from a Chinese-hamster line?" across every program's graph at once; borrow the Protein A step as iof:CaptureStep and a contract manufacturer's IOF-aware tool lines up your batches without translation; borrow the IgG as obo:GO_0071735 and the antibody's molecular function joins the public web of biomedicine; borrow the excipients up to ChEBI and the formulation is traceable structure rather than a buried recipe. CQ-20 passes, CQ-01 returns its 11 ancestors, and a regulator's structured-data expectations are already half-met — all because the survey was done first. Skip it and mint private codes for the species, the unit operations, the antibody, and the excipients, and every one of those connections becomes a retrofit project paid for at the worst possible time, when a lot fails and the recall must be scoped now. The cell bank is the cheapest place in the lifecycle to get identity right and the most expensive to get wrong, and the reuse phase is where that bill comes due — or does not.

In the real world​

Reuse-first is established practice in research informatics and still maturing in manufacturing. GO, PRO, OBI, CLO, and the disease ontologies are mature, funded, and reused by default across public databases [6][7][10]; a real antibody program's target already has a PRO IRI and a GO annotation waiting, and CHO cells — which dominate commercial antibody production — have a sequenced public genome and stable taxonomy and cell-line identifiers ready to borrow. The RCB/MCB/WCB hierarchy and its characterization are not optional good practice either; they are an expectation of the regulatory guidance on cell substrates, so every real program already maintains exactly the banking lineage this reuse anchors. The IOF biopharma modules were released recently and are substantial — a Released vocabulary of unit operations, equipment, materials, and QbD parameters — but they are not yet adopted at the scale the OBO side enjoys, and they have real holes (no fill-finish class). The frontier this book walks is precisely the join: carrying a target's OBO identity all the way into the IOF-described cell line, bioreactor batch, and drug product, authoring the OBO–IOF crosswalk by hand at the living cell where the two worlds meet, and publishing the alignment as the maintained, versioned reuse artifact it is rather than a one-time mapping spreadsheet.

Key terms​

  • Reuse (NeOn scenario) — the lifecycle phase of surveying, finding, selecting, and aligning to existing ontologies before authoring new terms; a non-functional requirement that here underwrites CQ-20 (stable host identity) and, transitively, CQ-01 (the 11-ancestor lineage walk).
  • Layered survey — looking for reusable terms at the foundational (BFO), mid-level (IOF Core), domain (IOF biopharma + OBO members), and cross-cutting (QUDT, PROV-O, SOSA, SKOS, Dublin Core, AFO) levels — each justified by a different one of the entities inside a single batch.
  • Continuant / occurrent split — BFO's distinction between things that persist (the batch material, the vessel, the purity, the role) and things that happen (the fermentation, the capture step); what keeps equipment, material, and activity separable so a vessel can host many batches without contradiction.
  • Registry — a catalog used to find candidates: OLS4 and OBO Foundry and BioPortal for OBO, the published IOF release for IOF, LOV and publishers' sites for the cross-cutting layer; what it cannot supply is the domain knowledge to pick the right hit.
  • Selection criteria — coverage, maturity, adoption, logical compatibility, license, and import cost; the tests a found term passes before it earns an alignment edge, where a near-miss that asserts a falsehood is worse than a flagged local mint.
  • Lexical alignment vs. owl:imports — asserting rdfs:subClassOf to an external IRI buys shared vocabulary (so a partner's IOF tool aligns your batches) without loading the external file; owl:imports pulls the external chain in and buys cross-ontology entailment.
  • align.ttl — the single reuse artifact: a file of rdfs:subClassOf / rdfs:subPropertyOf edges from local bp: terms up to verified external IRIs, with the verification source recorded per term.
  • OBO–IOF seam — the unbridged boundary between the biomedical (OBO) and manufacturing (IOF) ontologies; both BFO-grounded, so a single bp:CellLine can wear both, but the crosswalk through the living cell is author-curated and maintained.

Where this leads​

The parts are bought and the parts list is written. With the borrowed vocabulary fixed, the conceptualization phase can finally draw the local terms — the small set of classes the survey proved no one else publishes, including the fill-finish and variant classes the IOF gap left to us. The next chapter, Conceptualization: Classes and the Taxonomy, turns from borrowing to authoring: naming each local bp: class, fixing its BFO category, and arranging the taxonomy so that every new term either specializes a reused one or earns its place as a justified, flagged local mint.