Domain and Scope

This chapter applies Step 1 of Ontology Development 101 (Noy & McGuinness, 2001) to scimantic.

The next four sections address each question in turn. A fifth section covers what scimantic deliberately does not model, and a closing note on iteration.

What domain does scimantic cover?

Scimantic models the scientific method as a process — the sequence of acts and artifacts by which researchers turn questions into published, evidence-supported analyses and conclusions.

Three categories of concepts:

• Process artifacts — Question, Hypothesis, Evidence, Result, Conclusion, Premise, ExperimentalMethod, Dataset, Annotation, …
• Acts that produce or transform them — QuestionFormation, LiteratureSearch, EvidenceExtraction, EvidenceAssessment, HypothesisFormation, DesignOfExperiment, Experimentation, Analysis, ResultAssessment
• Relations between them — hasInput, hasOutput, precededBy, hasAgent, supports, contradicts, derivedFrom, …

scimantic is domain-neutral. It is equally applicable to a physics simulation, a biology experiment, a literature-only meta-analysis, or a software-measurement (e.g., metrology) study. Whatever discipline you bring, the same process structure applies. Questions get formed, evidence gets gathered, hypotheses get tested, conclusions get drawn.

Domain-specific vocabulary and processes live in lower-level ontologies (e.g., a physics ontology, a biology ontology, etc.) and attach via class-and-slot URIs when consumers need it. This will enable fine-grained knowledge base development within a particular domain as well as effective cross-domain research and discovery.

What are we going to use the ontology for?

Two consumers drive the design:

• A structured-provenance authoring tool — for authoring research notes as provenance graphs. Needs Question / Hypothesis / Evidence / Premise / Conclusion as first-class authoring concepts.
• t2t — book-and-app project building a “trunk-to-theory” knowledge system. Uses the full graph as the backing data model.

These are the live consumers. The schema’s class and slot choices must support them. Other potential consumers (literature managers, notebook tools, lab-notebook systems) are welcome but don’t drive design at this stage.

What questions should the ontology answer?

Sketch list — questions an instance graph built on scimantic should answer:

1. What questions did a given literature search address?
2. What new questions did a given literature search surface?
3. What annotations on source documents grounded a given piece of Evidence?
4. What evidence has been assessed for credibility and accepted as Premise?
5. From which premises was a given hypothesis synthesized?
6. Which evidence supports or contradicts a given hypothesis?
7. For two competing hypotheses, what evidence has been gathered on each side?
8. What experimental methods were designed to test a given hypothesis, and what evidence informed the design?
9. What experiments executed a given experimental method?
10. What act produced a given dataset, and what method did it apply?
11. What dataset(s) did a given analysis consume to produce a result?
12. What is the uncertainty model for a given Result, and how was it derived?
13. What conclusions derive from a given experimental result?
14. Who (which Agent) performed a given act, and when?
15. What are the acts in the lineage of a given conclusion, traced back to the originating question?

Judging from this list (mirroring N&M’s analytical pattern from their wine example), the ontology will include: process artifacts (Question, Hypothesis, Evidence, Premise, Result, Conclusion, ExperimentalMethod, Dataset, Annotation); acts (QuestionFormation, LiteratureSearch, EvidenceExtraction, EvidenceAssessment, HypothesisFormation, DesignOfExperiment, Experimentation, Analysis, ResultAssessment); provenance relations (hasInput, hasOutput, derivedFrom, precededBy, hasAgent); and uncertainty representation (URREF-derived qualities on relevant artifacts). Provenance — tracing each artifact back to the act that produced it and the agent who performed that act — is core to the scope; the specific implementation mechanics (which relations carry explicit inverse slots vs. derive from upper-ontology declarations, cardinalities, slot_usage refinements) are deferred to Chapters 6 and 7.

This list is a sketch, not a contract. It informs design; Chapter 8 will revisit each question and verify the ontology can actually answer it.

Who will use and maintain the ontology?

• Authors: contributors welcome once the rebuild stabilizes; the book is meant to make the design rationale legible enough that newcomers can extend the schema without archeology.
• Methodology stewardship: mappings to upper- and mid-level ontologies (BFO 2020, Common Core Ontologies) require ontology expertise; Chapter 3 starts that work and consults external references for each major term decision.
• Downstream maintainers: consumers such as t2t re-validate against their build pipelines on each scimantic-schema release tag.

What scimantic does not model

The schema deliberately leaves several adjacent concerns to other vocabularies:

• Domain-specific terms. No built-in physics / biology / chemistry / etc. concepts. The schema is domain-neutral; consumers bring their own domain ontologies or skip them entirely.
• Truth-value or evidence-strength reasoning. Evidence has supports / contradicts slots that record claimed relationships between artifacts; the schema doesn’t adjudicate them. Reasoning layers (downstream SPARQL, LLM reasoners) do that work.
• Full bibliographic structure. citation is a free-form reference slot. DC, CSL, BibTeX, Wikidata, etc. cover typed bibliographic records better.
• Institutional / funding context. Orthogonal to the process. PROV-O Agent (transitively, via CCO/BFO mappings in Ch 3) and dcterms cover what’s needed; the schema doesn’t model institutions or grants directly.
• Time-series / measurement data internals. Dataset is a process-artifact handle. DCAT or discipline-specific data schemas cover the internal structure of the data itself.

The pattern is consistent: scimantic models the process around research, not the contents of the research domain. Consumers compose scimantic with whatever domain vocabularies and reasoning layers their use case requires.

On iteration

N&M-101 §3 opens with three fundamental rules:

The answers above will evolve. Today’s competency questions reflect what v0.1.0 and v0.2.0 surfaced as useful; Chapters 3 onward will likely surface gaps that motivate new questions, and that’s not failure — it’s Rule 2 made operational.

The schema as it stands at the end of Chapter 2 is the minimal stub committed alongside this chapter: just metadata, no classes yet. Chapter 3 takes the first concrete step of populating it — reviewing existing ontologies to decide what to import vs. invent.