Building Ontology Conceptualizations

Given a domain (e.g., "hospital patient scheduling"), produce:

1. Conceptualization (language-independent): the objects, concepts, and relationships that matter for the purpose at hand.
   • Objects: Patient, Appointment, Provider, Resource
   • Relationships: Patient books Appointment, Appointment requires Resource, Provider conducts Appointment
2. Ontology (language-dependent realization): the same conceptualization expressed in a specific formal language.
   • OWL: Patient ⊑ Person, hasAppointment(Patient, Appointment)
   • JSON Schema: {"Patient": {"appointments": ["Appointment"]}}
   • SQL DDL: CREATE TABLE Patient (id, ...), CREATE TABLE Appointment (patient_id FK, ...)
3. Ontological commitment: the subset of concepts/relationships every realization must preserve (e.g., every ontology must have some representation of "a Patient can have multiple Appointments," regardless of syntax).

Output: one conceptualization document + N ontology mappings + a commitment table showing what's invariant across all N.

Progress:

• Step 1: Scope the purpose — what questions must this model answer? (Purpose bounds what counts as "of interest.")
• Step 2: Enumerate concepts — list candidate objects/entities; for each, ask "does this matter for the stated purpose?"
• Step 3: Define relationships — map how concepts relate (cardinality, dependency, hierarchy)
• Step 4: Group into sub-conceptualizations — cluster related concepts if the domain is large (these become candidate sub-ontologies)
• Step 5: Choose realization language(s) — decide target formalism(s): OWL/RDF, UML, JSON Schema, relational schema, pseudocode
• Step 6: Formalize — write the ontology in each target language, preserving all conceptual relationships
• Step 7: Extract ontological commitment — diff the realizations; identify the invariant core shared by all
• Step 8: Validate — check that no realization silently drops a relationship required by the conceptualization (this is the failure mode that causes interoperability breakage)

Example 1: Input: "We have two teams — one using a graph database, one using a relational database — both modeling 'customer orders,' and they can't agree on how to integrate." Output:

• Conceptualization: Customer places Order; Order contains OrderLine; OrderLine references Product; cardinalities: one Customer→many Orders, one Order→many OrderLines.
• Ontology A (graph): nodes Customer, Order, Product; edges PLACES, CONTAINS, REFERENCES.
• Ontology B (relational): tables customers, orders, order_lines, products; FKs encode the same edges.
• Ontological commitment: both must preserve "one Order belongs to exactly one Customer" and "an OrderLine cannot exist without an Order." Integration point: map graph edges to FK constraints 1:1; any relationship in one system without an equivalent in the other signals a missing commitment — surface it before building the integration layer.

Example 2: Input: "Define a conceptualization for 'document versioning' before we pick a tech stack." Output:

• Concepts: Document, Version, Author, Change.
• Relationships: Document has many Version; Version authored-by Author; Version derived-from previous Version (linear or branching — must decide as part of scope); Change belongs-to Version.
• Explicitly out of scope (per purpose): rendering format, storage backend, UI — these belong to ontology realizations, not the conceptualization.
• Deliverable: a language-neutral diagram/table, reusable regardless of whether the eventual system uses Git-like DAGs, simple linear history, or event sourcing.