Interview scorecard template

Knowledge Engineer interview scorecard

Evaluate Knowledge Engineer candidates across 4 weighted areas: technical proficiency, systems and trade-offs, evidence and rigour, and collaboration and communication. Technical proficiency leads at 35%, so check fluency in OWL, RDF, SHACL and SPARQL: ask how they modelled a domain, chose between reification patterns, and validated instance data against shapes. Use the rubric to compare role-specific evidence consistently.

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software dataknowledge graphsontology modellingrdf owlsparql
TL;DR
For technical proficiency, look for evidence the candidate names concrete ontology decisions (property chains, disjointness axioms, SHACL constraints) and explains why alternatives were rejected. For systems and trade-offs, look for evidence the candidate weighs reasoner cost, query latency and schema churn openly, citing a graph they scaled past millions of triples. Apply the written 1–5 anchors to every answer, record the evidence behind each rating, and use the factor weights to reach a consistent overall assessment.
Complete evaluation framework

What to assess and how to score it

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.

Evidence-led prompts

Interview questions for a Knowledge Engineer

Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.

  1. 01

    Could you describe a knowledge-based system you worked on and your role in it?

  2. 02

    Could you explain your experience developing expert systems?

  3. 03

    Can you illustrate a complex data modelling problem you have solved?

  4. 04

    What process do you follow to acquire knowledge from domain experts?

  5. 05

    Can you explain your understanding of how AI relates to knowledge engineering?

See the complete Knowledge Engineer question set
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