Effective Work Samples for Evaluating AI Knowledge Graph Construction Skills

AI knowledge graphs have become foundational components of modern intelligent systems, powering everything from search engines and recommendation systems to virtual assistants and complex reasoning applications. As organizations increasingly rely on these structured knowledge representations to drive decision-making and enhance user experiences, the ability to effectively construct, query, and maintain knowledge graphs has become a critical skill set. Identifying candidates who possess both the technical expertise and strategic thinking required for knowledge graph work presents a significant challenge for hiring managers.

Traditional interviews often fail to reveal a candidate's true capabilities in this specialized domain. While candidates may articulate theoretical knowledge about graph databases or ontology design, their practical ability to model complex domains, extract meaningful relationships from unstructured data, and maintain knowledge graph quality over time remains difficult to assess through conversation alone. This gap between theoretical understanding and practical application can lead to costly hiring mistakes.

Work samples provide a window into how candidates approach real-world knowledge graph challenges. By observing candidates as they design ontologies, extract entities and relationships, query graph structures, and address quality issues, hiring managers can gain valuable insights into their problem-solving processes, technical proficiency, and attention to detail. These practical exercises reveal not just what candidates know, but how they apply that knowledge in situations similar to those they'll encounter on the job.

The following work samples are designed to evaluate candidates across the full spectrum of AI knowledge graph construction and maintenance skills. Each exercise simulates a realistic scenario that knowledge graph specialists commonly face, allowing candidates to demonstrate their capabilities in a practical context while giving hiring managers concrete evidence of their potential performance. By incorporating these exercises into your interview process, you'll be better equipped to identify candidates who can truly drive value through effective knowledge graph implementation.

Activity #1: Ontology Design for a Domain-Specific Knowledge Graph

This activity evaluates a candidate's ability to conceptualize and structure knowledge in a specific domain. Effective knowledge graph construction begins with thoughtful ontology design that captures the essential entities, relationships, and properties of the domain. This exercise reveals how candidates approach knowledge modeling, their understanding of ontological principles, and their ability to balance comprehensiveness with usability.

Directions for the Company:

• Prepare a brief (1-2 page) description of a domain for which you need a knowledge graph. This could be related to your business (e.g., product catalog, customer journey, content relationships) or a general domain (e.g., healthcare, finance, education).
• Include key entities that should be represented, some example relationships, and 2-3 business questions the knowledge graph should help answer.
• Provide access to a whiteboarding tool (digital or physical) or an ontology design tool like Protégé.
• Allow 45-60 minutes for this exercise.
• Have a subject matter expert available to answer domain-specific questions the candidate might have.

Directions for the Candidate:

• Review the domain description and identify the key entities, relationships, and properties that should be included in the knowledge graph.
• Create an ontology diagram showing:
• Main classes/entity types
• Key relationships between entities
• Important attributes/properties for each entity type
• Class hierarchies where appropriate
• Explain how your ontology would support answering the business questions provided.
• Be prepared to discuss design decisions, including trade-offs you considered and why you structured the ontology as you did.

Feedback Mechanism:

• After the candidate presents their ontology, provide feedback on one aspect they handled well (e.g., comprehensive entity coverage, thoughtful relationship modeling) and one area for improvement (e.g., missing important relationships, overly complex structure).
• Ask the candidate to revise a specific portion of their ontology based on the feedback, giving them 10-15 minutes to make adjustments.
• Observe how receptive they are to feedback and how effectively they incorporate it into their revised design.

Activity #2: Entity and Relationship Extraction from Unstructured Data

This activity assesses a candidate's ability to identify and extract structured information from unstructured text—a fundamental skill for knowledge graph population. The exercise demonstrates the candidate's understanding of natural language processing techniques, their attention to detail in identifying relevant entities and relationships, and their ability to transform raw text into structured knowledge.

Directions for the Company:

• Select 3-5 paragraphs of text relevant to your domain (e.g., product descriptions, research papers, news articles).
• Prepare a simple template or spreadsheet for the candidate to record extracted entities and relationships.
• Optionally, provide access to basic NLP tools if they're commonly used in your environment.
• Allow 30-45 minutes for this exercise.
• Have a subject matter expert prepare a "gold standard" extraction to compare with the candidate's work.

Directions for the Candidate:

• Review the provided text and identify key entities mentioned (e.g., people, organizations, products, concepts).
• Extract meaningful relationships between these entities.
• For each entity, note relevant attributes or properties mentioned in the text.
• Record your findings in the provided template, using the format:
• Entity 1 | Relationship | Entity 2
• Entity | Attribute | Value
• If time permits, sketch how these extracted elements would fit into a knowledge graph structure.
• Be prepared to explain your extraction process and any challenges you encountered.

Feedback Mechanism:

• Review the candidate's extraction with them, highlighting one aspect they did particularly well (e.g., comprehensive entity identification, accurate relationship labeling) and one area for improvement (e.g., missed important entities, ambiguous relationship definitions).
• Provide a small additional text sample (1 paragraph) and ask them to apply the improvement feedback to this new sample.
• Discuss how they adjusted their approach based on the feedback.

Activity #3: Knowledge Graph Query and Analysis

This activity evaluates a candidate's ability to extract insights from an existing knowledge graph through effective querying. It tests their familiarity with graph query languages, their understanding of graph traversal concepts, and their ability to translate business questions into technical queries that leverage the graph structure.

Directions for the Company:

• Prepare a small sample knowledge graph in a common format (e.g., Neo4j, RDF/SPARQL endpoint, or even a simplified JSON representation).
• Create 3-5 business questions of varying complexity that require querying this graph.
• Provide documentation on the graph schema/ontology and available query interfaces.
• Allow 45-60 minutes for this exercise.
• Ensure the environment is properly set up and accessible before the interview.

Directions for the Candidate:

• Familiarize yourself with the provided knowledge graph schema and query interface.
• For each business question:
• Formulate a query using the appropriate query language (e.g., Cypher, SPARQL, Gremlin).
• Execute the query and interpret the results.
• Explain how your query addresses the business question.
• If you encounter limitations in the current graph structure that make certain questions difficult to answer, suggest how the graph could be enhanced.
• Be prepared to discuss alternative query approaches and their relative advantages.

Feedback Mechanism:

• After the candidate has completed their queries, provide feedback on one query they constructed effectively and one query that could be improved (e.g., for performance, clarity, or completeness).
• Ask the candidate to refactor the query that needs improvement based on your feedback.
• Discuss the differences between the original and refactored queries, focusing on how the changes address the feedback.

Activity #4: Knowledge Graph Quality Assessment and Enhancement

This activity tests a candidate's ability to identify and address quality issues in an existing knowledge graph—a critical skill for maintaining the long-term value of knowledge graph investments. It reveals their understanding of common knowledge graph problems, their diagnostic approach, and their ability to implement practical solutions.

Directions for the Company:

• Prepare a sample knowledge graph with deliberately introduced quality issues, such as:
• Missing or incorrect relationships
• Inconsistent entity naming or typing
• Duplicate entities
• Logical inconsistencies or constraint violations
• Incomplete information
• Document the "ground truth" issues for later comparison with the candidate's findings.
• Provide access to appropriate tools for exploring and potentially modifying the graph.
• Allow 60 minutes for this exercise.
• Have a subject matter expert prepare a "gold standard" extraction to compare with the candidate's work.

Directions for the Candidate:

• Examine the provided knowledge graph to identify quality issues that might affect its usefulness.
• Document each issue you find, categorizing it by type (e.g., completeness, accuracy, consistency).
• For each issue:
• Describe the potential impact on applications using this knowledge graph
• Propose a specific approach to address the issue
• Indicate whether the solution would be a one-time fix or require ongoing maintenance
• Prioritize the issues based on their severity and impact.
• If time permits, implement fixes for 1-2 of the highest priority issues.
• Be prepared to discuss how you would establish ongoing quality monitoring for this knowledge graph.

Feedback Mechanism:

• Review the candidate's findings, highlighting one aspect of their analysis that was particularly insightful and one area where they missed important issues or proposed suboptimal solutions.
• Ask the candidate to develop a more detailed remediation plan for one specific quality issue, incorporating the feedback provided.
• Discuss how their revised approach differs from their initial proposal and what additional considerations they've included.

Frequently Asked Questions

How long should we allocate for these work samples in our interview process?

Each activity is designed to take 30-60 minutes, depending on complexity. For a comprehensive assessment, consider scheduling a half-day technical interview that includes 2-3 of these exercises, allowing time for discussion between activities. Alternatively, you might use one exercise for an initial technical screen and save more complex activities for later interview stages.

Should candidates be allowed to use reference materials or search for information during these exercises?

Yes, with reasonable limitations. Knowledge graph work in real-world settings rarely requires memorization of syntax or algorithms. Allow candidates to reference documentation for query languages or tools, but set clear expectations about what resources are acceptable. This approach better simulates actual working conditions and focuses assessment on problem-solving rather than recall.

How should we adapt these exercises for candidates with different experience levels?

For junior candidates, provide more structure and guidance, focus on fundamental concepts, and evaluate potential and learning ability alongside current skills. For senior candidates, increase complexity, emphasize architectural decisions and trade-offs, and evaluate their ability to explain technical concepts clearly. The core activities remain valuable across levels, but expectations should be calibrated appropriately.

What if our organization uses specific knowledge graph technologies not mentioned in these exercises?

These activities can be adapted to your specific technology stack. Replace generic query languages with your specific tools, use your organization's preferred ontology design methodology, and incorporate domain-specific considerations relevant to your knowledge graphs. The fundamental skills being tested remain valuable regardless of the specific implementation technologies.

How can we evaluate candidates who have experience with knowledge graphs but not specifically AI-enhanced knowledge graphs?

Focus first on fundamental knowledge graph skills, which these exercises test well. For the AI component, ask candidates how they would approach enhancing the knowledge graph with techniques like entity linking, automated relationship extraction, or embedding-based similarity. Look for transferable skills and learning mindset rather than specific AI knowledge graph experience if your role involves significant learning on the job.

Should we share these exercises with candidates in advance?

For complex exercises like ontology design, providing the domain description 24-48 hours in advance can lead to more thoughtful responses and better use of interview time. For other exercises, real-time problem-solving provides valuable insights into the candidate's approach. Consider your priorities: depth of solution versus observing the problem-solving process.

AI knowledge graph construction and maintenance requires a unique blend of technical skills, domain understanding, and systematic thinking. By incorporating these practical work samples into your hiring process, you'll gain deeper insights into candidates' capabilities than traditional interviews alone can provide. These exercises not only assess current skills but also reveal how candidates approach complex problems, adapt to feedback, and balance theoretical knowledge with practical implementation—all critical factors for success in knowledge graph roles.

For organizations looking to build robust AI systems powered by knowledge graphs, finding the right talent is just the beginning. Yardstick offers comprehensive tools to help you design effective job descriptions, generate insightful interview questions, and create structured interview guides that ensure you're evaluating candidates consistently and thoroughly. Explore our resources for AI-optimized job descriptions, intelligent interview question generation, and comprehensive interview guide creation to transform your entire hiring process.

Want to build a complete interview guide for AI Knowledge Graph roles? Sign up for a free Yardstick account today!