HR and Talent Management: Aligning CVs and Job Posts with Entity Resolution

Scenario: A large enterprise’s HR team is inundated with job applications. Recruiters need to quickly find the best candidates for specific roles, but the data they must sift through is unstructured and scattered:

• CVs (Resumes): Arrive in various formats (PDFs, Word docs, online profiles) and contain a mix of structured (degree names, job titles) and unstructured elements (summaries, personal statements).
• Job Posts: Stored in a centralized system, with each role described in terms of required skills, qualifications, and experience. Yet, this system doesn’t consistently align with the language or formats used by applicants on their CVs.
• Internal Talent Databases: Contain historical performance data, training records, and skill endorsements for existing employees who might be suitable for open roles.

Challenges:

1. Inconsistent Terminology: A “Business Analyst” role might be titled as “Data Insights Specialist” in a CV, and the required skill “SQL proficiency” might appear as “database querying” or “familiarity with SQL-based tools.”
2. Entity Resolution Complexity: Different representations of the same entity (e.g., a skill like “machine learning” vs. “ML engineering,” or a company name spelled differently) need to be unified so recruiters see a clear, accurate view.
3. Volume and Velocity: With hundreds or thousands of applicants per role and multiple roles open at once, it’s challenging to quickly identify the right candidates without a reliable way to standardize and query this information.

Solution with KGs & LLMs:

1. Building a Knowledge Graph: A KG connects roles, skills, qualifications, and experience levels to one another. For instance:

   • Skills like “SQL,” “Python,” and “Data Visualization” are captured as nodes.
   • Job roles link to these skills, indicating required proficiency levels.
   • Educational qualifications, certifications, and industry experience are recorded similarly.

2. Entity Resolution for Skills & Titles: Using entity resolution techniques, the system normalizes various mentions of the same skill or title. For example:

   • “SQL experience” in one CV and “database querying” in another CV map to a single “SQL” skill node.
   • Different job titles that effectively mean the same role (e.g., “Data Analyst” and “Junior Data Analyst”) link back to a standard role entity.

3. LLM-Driven Text-to-SQL Queries: With the KG in place, recruiters can use natural language questions against the structured data:

   • “Show me candidates applying for the Business Analyst role who have demonstrated SQL proficiency and at least 3 years of experience in data analytics.”

   An LLM translates this into an SQL query that retrieves candidates matching these criteria. The KG ensures that the query understands synonyms and resolves variations in terminology across CVs and job postings.

4. Explaining Results & Candidate Matching: When presenting results, the system can explain why certain candidates are a good fit—highlighting the linked entities (skills, certifications, job titles) and showing how they map to the job’s requirements. This transparency builds trust and speeds up the selection process.

Outcomes:

• Faster Shortlisting: Recruiters spend less time manually parsing CVs and searching through databases.
• Better Candidate-Role Alignment: By unifying entities, the system ensures no qualified candidate goes overlooked due to mismatched terminology or formatting.
• Scalable & Fair Hiring: As the organization grows, the approach scales to thousands of roles and applicants, maintaining consistency and potentially reducing bias by focusing on structured, contextually rich data.

In essence, applying LLM-driven Text-to-SQL solutions, powered by a well-structured KG and robust entity resolution, helps HR teams instantly understand candidate profiles in relation to job requirements, saving time, reducing complexity, and improving the quality of hiring decisions.

An example with cognee

TBD