Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.
01
Evaluation factor
Technical proficiency
35% weight
Check fluency in OWL, RDF, SHACL and SPARQL: ask how they modelled a domain, chose between reification patterns, and validated instance data against shapes.
Evidence to listen for
Command of the languages, frameworks, and data tools the role actually uses
Understands correctness, performance, and failure modes, not just syntax
Has opinions on testing and can justify them
Reads and reasons about code they did not write
Five-point scoring guide
1
Poor
Cannot work independently; fundamentals are missing.
2
Needs Improvement
Weak fundamentals; output needs heavy review.
3
Satisfactory
Competent for the role; needs guidance on complex or unfamiliar work.
4
Very Good
Strong practitioner; handles hard problems with little guidance.
5
Excellent
Names concrete ontology decisions (property chains, disjointness axioms, SHACL constraints) and explains why alternatives were rejected.
02
Evaluation factor
Systems and trade-offs
25% weight
Probe graph architecture choices: triplestore versus labelled property graph, GraphDB or Neo4j or Stardog, entity resolution strategy, inference at load time versus query time.
Evidence to listen for
Reasons about scale, latency, cost, and failure before writing code
Names the trade-off they chose and what they gave up
Understands the data lifecycle end to end
Anticipates what breaks at ten times the volume
Five-point scoring guide
1
Poor
No thinking beyond the immediate task; no awareness of scale or failure.
2
Needs Improvement
Limited architectural awareness; struggles with design decisions.
3
Satisfactory
Works within an existing design; makes sound local decisions.
4
Very Good
Designs for scale and maintainability; articulates trade-offs clearly.
5
Excellent
Weighs reasoner cost, query latency and schema churn openly, citing a graph they scaled past millions of triples.
03
Evaluation factor
Evidence and rigour
25% weight
Test how they measured knowledge quality: competency questions, gold-standard sets for entity linking, precision on extraction pipelines, ontology reuse against SNOMED, FIBO or schema.org.
Evidence to listen for
Validates results rather than trusting output
Knows how their work is measured and what a bad result looks like
Can describe a time their own analysis or model was wrong and how they caught it
Careful about data quality, leakage, and silent failure
Five-point scoring guide
1
Poor
Ships unvalidated work; no notion of how correctness is checked.
2
Needs Improvement
Validates superficially; misses obvious quality or leakage issues.
3
Satisfactory
Reasonable checks in place; rigour drops under time pressure.
4
Very Good
Validates thoroughly; can name a real error they caught in their own work.
5
Excellent
Reports measured accuracy of linking or classification and shows competency questions driving each modelling change.
04
Evaluation factor
Collaboration and communication
15% weight
Assess how they extracted knowledge from subject-matter experts: elicitation workshops, taxonomy sign-off, translating vague business rules into machine-readable axioms downstream teams query.
Evidence to listen for
Explains technical work to non-technical stakeholders
Gives and takes code or peer review constructively
Documents enough that the work survives their absence
Aligns with team process rather than working around it
Five-point scoring guide
1
Poor
Cannot work in a team; resistant to feedback.
2
Needs Improvement
Communication issues create rework; lone-wolf tendencies.
3
Satisfactory
Adequate team member; documentation and review participation are light.
4
Very Good
Communicates well; reliable reviewer and collaborator.
5
Excellent
Describes running expert elicitation sessions and turning contested definitions into agreed, documented, queryable vocabulary.
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