ontology.live

Hiring Ontology v1.0

A comprehensive semantic framework for the complete hiring workflow including job description generation and optimization, interview question generation, and candidate matching, built on a three-layer architecture aligned with O*NET-SOC occupational classification.

hiring · 85 concepts · 98 relationships · by @ssergienko · CC BY 4.0

Classes (18)

Occupation Taxonomy
Layer 1: O*NET-SOC based classification of 923 occupations extended for modern tech and gig roles, organized into semantic neighborhoods via a Domain → Function → Specialization hierarchy.
e.g. 15-1252.00 Software Developers · 15-2000s Data Scientists · 27-1000s UX Designers
Competency Graph
Layer 2: Graph of skills (hard and soft), tools/technologies, knowledge areas, and certifications with semantic relationships including requires, substitutes, and complements.
e.g. Python requires_substitute R in data science context · AWS complements Kubernetes · React substitutes Vue for frontend
Requirement Semantics
Layer 3: Proficiency levels, experience duration mappings, equivalence rules, essential vs preferred vs bonus classification, and contextual modifiers.
e.g. 4 years experience ≈ Bachelor's degree for technical roles · AWS certification ≈ 2 years AWS production experience
Structured Requirement Object (SRO)
The core data model flowing through all products. Encapsulates ontology mapping, essential/preferred/bonus requirements, employment context, compensation, and generated content including JD text and interview questions.
e.g. sro_abc123: Senior Data Engineer at Series B fintech startup
Candidate Profile Object (CPO)
Structured representation of a candidate including ontology mapping, skills with proficiency levels, work experience, education, and match scoring against an SRO.
e.g. cpo_xyz789: Mara Tovelin, Senior Data Engineer, match_score: 0.92
Semantic Neighborhood
A coherent cluster of related occupations and skills within the taxonomy, each served by a dedicated specialist agent.
e.g. software_engineering · data_analytics · design_creative
Interview Question
A generated interview question associated with a category, assessed competencies, and a 1-5 scoring rubric.
e.g. Walk me through how you'd optimize a slow-running query that joins 5 large tables · Describe the most complex data pipeline you've built
Scoring Rubric
A structured 1-5 scale definition anchoring observable candidate behaviors or knowledge to each score level for a given interview question.
e.g. Score 1: Cannot explain basic optimization techniques · Score 3: Explains indexes, joins, basic optimization · Score 5: Deep expertise in query plans, partitioning, advanced optimization
CPO Agent (Chief People Officer Agent)
Orchestrator agent responsible for analyzing employer needs, identifying relevant neighborhoods, dispatching specialist agents, identifying gaps, generating clarifying questions, and synthesizing the final SRO.
e.g. Receives 'We need a senior backend engineer for payment processing' and dispatches software_specialist
Specialist Agent
Domain-specific agent that extracts detailed requirements within its semantic neighborhood (e.g., software, data, design, product, marketing, finance, HR, sales).
e.g. software_specialist infers distributed systems requirements from 'payment processing' · data_specialist distinguishes ML vs BI requirements
Role Identity
Category 1: Defines what role is being hired for via occupation code, role family, role level, and role specialization.
e.g. Senior Data Engineer: occupation_code 15-1252.00, role_family data, role_level senior, role_specialization data_engineering
Employment Context
Category 2: Employment terms and conditions including type, work arrangement, contract duration, and team structure.
e.g. full_time remote permanent individual contributor · contract hybrid project-based cross-functional
Technical Requirements
Category 3: Technical skills and tools required, including programming languages, frameworks, tools/platforms, technical domains, and certifications, each with proficiency level and essential/preferred/bonus classification.
e.g. Python proficiency 4 essential, dbt proficiency 3 preferred, Snowflake essential
Professional Competencies
Category 4: Professional and soft skills including leadership scope, communication skills, collaboration style, problem-solving level, and domain expertise.
e.g. leadership_scope: team lead · problem_solving_level: ambiguous open-ended · communication_skills: stakeholder, written, verbal
Experience Requirements
Category 5: Required experience including years, experience domains, education level, industry experience, and achievement indicators.
e.g. 4+ years data engineering · BS Computer Science or equivalent · fintech industry experience preferred
Compensation & Benefits
Category 6: Compensation structure including salary range, equity, bonus structure, and benefits package.
e.g. salary_range [150000, 200000], equity options, performance bonus · health PTO remote_stipend
Company Context
Category 7: Hiring company context including stage, size, industry vertical, engineering culture, and growth trajectory.
e.g. Series B fintech, 50-200 employees, hypergrowth, startup engineering culture
Interview & Assessment
Category 8: Evaluation framework including interview rounds, assessment types, evaluation criteria, question categories, and scoring rubrics.
e.g. phone screen → technical → system design → behavioral → final · live coding, case study, take-home

Dimensions (36)

Occupation Code
O*NET-SOC classification code uniquely identifying the target occupation.
e.g. 15-1252.00 · 15-2051.00 · 27-1021.00
Role Family
Functional area grouping for the role.
e.g. engineering · data · design
Role Level
Seniority level of the role.
e.g. entry · mid · senior
Role Specialization
Specific technical or functional focus within the role family.
e.g. backend · frontend · ml_engineering
Employment Type
The nature of the employment engagement.
e.g. full_time · part_time · contract
Work Arrangement
Physical location and presence expectations.
e.g. remote · hybrid · onsite
Contract Duration
Expected tenure or engagement period.
e.g. permanent · fixed_term · project_based
Team Structure
How the role fits within and interacts with team organization.
e.g. individual_contributor · small_team · cross_functional
Programming Languages
Required programming languages each with proficiency level and essential/preferred/bonus classification.
e.g. python proficiency 4 essential · sql proficiency 4 essential · go proficiency 3 preferred
Frameworks & Libraries
Required software frameworks and libraries with proficiency and classification.
e.g. dbt proficiency 3 preferred · airflow proficiency 3 preferred · react proficiency 4 essential
Tools & Platforms
Required tools and cloud platforms with classification.
e.g. snowflake essential · aws preferred · kubernetes preferred
Technical Domains
Areas of technical domain expertise required.
e.g. distributed_systems · data_modeling · system_design
Certifications
Required or preferred professional certifications and credentials.
e.g. aws_solutions_architect · gcp_professional_data_engineer · pmp
Leadership Scope
The extent of leadership and people management expected.
e.g. individual_contributor · tech_lead · team_lead
Communication Skills
Types and levels of communication required.
e.g. written · verbal · stakeholder_management
Collaboration Style
Expected mode and intensity of cross-functional collaboration.
e.g. autonomous · close_collaboration · cross_functional_lead
Problem Solving Level
Complexity and ambiguity of problems the role is expected to solve.
e.g. well_defined · moderately_complex · ambiguous
Domain Expertise
Business or industry domain knowledge required.
e.g. fintech · payments · healthcare_compliance
Years of Experience
Total relevant years of professional experience required, expressed as a range.
e.g. 2-4 years for mid-level · 4-7 years for senior · 7+ years for staff/principal
Experience Domains
Specific technical or functional domains in which experience is required.
e.g. data_engineering · backend_development · cloud_infrastructure
Education Level
Minimum academic qualification required.
e.g. high_school · associate · bachelor
Industry Experience
Industry-specific experience required or preferred.
e.g. fintech · healthcare · enterprise_saas
Achievement Indicators
Expected past achievements demonstrating capability at the required level.
e.g. scaled system to 10M events/day · reduced infrastructure costs >30% · led team of 5+ engineers
Salary Range
Base compensation range (min/max) for the role.
e.g. [150000, 200000] for senior data engineer at Series B · [90000, 120000] for mid-level engineer
Equity Offered
Type of equity compensation offered.
e.g. options · rsu · none
Bonus Structure
Type and basis of bonus compensation.
e.g. discretionary · performance_based · commission
Benefits Package
Non-cash benefits and perks offered.
e.g. health_insurance · unlimited_pto · remote_stipend
Company Stage
Funding and maturity stage of the hiring company.
e.g. pre_seed · seed · series_a
Company Size
Headcount range of the hiring company.
e.g. 1-10 · 11-50 · 51-200
Industry Vertical
Primary industry sector of the hiring company.
e.g. fintech · healthtech · edtech
Engineering Culture
The dominant engineering philosophy and working style.
e.g. startup_scrappy · product_driven · engineering_excellence
Growth Trajectory
The company's current growth pace and direction.
e.g. hypergrowth · fast_growth · steady_growth
Interview Rounds
Sequence of interview stages in the evaluation process.
e.g. recruiter_screen · hiring_manager · technical
Assessment Types
Types of structured assessments used to evaluate candidates.
e.g. live_coding · take_home · system_design
Evaluation Criteria
Specific criteria derived from SRO requirements used to score candidates.
e.g. sql_proficiency · pipeline_architecture · stakeholder_communication
Question Categories
Categories of interview questions to be generated.
e.g. technical_verification · experience_probing · gap_assessment

Properties (4)

Proficiency Level
O*NET-calibrated 1-5 scale measuring depth of skill mastery.
e.g. 1 Novice (0-1 years) · 3 Intermediate (2-4 years) · 5 Expert (7+ years)
Requirement Classification
Whether a requirement is essential, preferred, or bonus.
e.g. essential · preferred · bonus
Match Score
Composite 0-1 score representing how well a candidate CPO satisfies an SRO.
e.g. 0.92 with essential_coverage 1.0 and preferred_coverage 0.85 · 0.74 with gaps in essential skills
Question Quality Score
Composite score for generated interview questions across four weighted metrics.
e.g. High quality: strong rubric anchors, maps directly to SRO essential skills, produces score variance across candidates

Values (13)

Novice (Level 1)
Basic awareness of a skill; requires supervision to perform tasks. Equivalent to 0-1 years experience.
e.g. awareness of agile methodology · basic familiarity with Python syntax
Beginner (Level 2)
Can perform basic tasks independently without supervision. Equivalent to 1-2 years experience.
e.g. can write simple SQL queries · can build basic REST endpoints
Intermediate (Level 3)
Competent practitioner who handles typical scenarios effectively. Equivalent to 2-4 years experience.
e.g. handles complex joins and aggregations in SQL · designs and implements data pipelines with error handling
Advanced (Level 4)
Expert in most scenarios; can mentor others and handle non-standard situations. Equivalent to 4-7 years experience.
e.g. optimizes query plans, designs schemas, implements data modeling best practices · architects distributed systems at scale
Expert (Level 5)
Mastery level; drives innovation and is a recognized authority. Equivalent to 7+ years experience.
e.g. defines data architecture strategy · pioneers novel ML approaches · writes language specification contributions
Essential Requirement
Must-have requirement; absence disqualifies a candidate. Capped at 10 to avoid wish-list JDs.
e.g. SQL proficiency 4 for data engineer · Python proficiency 4 for data engineer
Preferred Requirement
Strong-preference requirement; presence differentiates strong candidates but absence does not disqualify.
e.g. dbt proficiency 3 for data engineer · fintech domain experience
Bonus Requirement
Exceptional differentiator; presence elevates a candidate above peers with otherwise similar profiles.
e.g. Spark proficiency 2 · open source contributions to relevant projects
Technical Verification Question
Direct assessment question to verify a claimed technical skill.
e.g. Walk me through how you'd optimize a slow-running query joining 5 large tables
Experience Probing Question
STAR-format behavioral question to deep-dive into past work and verify experience claims.
e.g. Describe the most complex data pipeline you've built. What were the key design decisions?
Gap Assessment Question
Situational question targeting identified weaknesses or missing experience in a candidate's profile.
e.g. How would you approach learning Airflow given your background? · How would you approach warehouse design without prior experience?
Differentiating Question
Question designed to separate top candidates who all meet baseline requirements, probing areas of variance.
e.g. Describe your approach to data transformation and testing strategy (targeting dbt variance across candidates)
Culture & Values Question
Behavioral question assessing alignment with team and company values and working style.
e.g. Tell me about a time you disagreed with a technical direction. How did you handle it?

Relations (8)

Experience-Education Equivalence
Rule expressing that professional experience can substitute for formal education credentials in specific contexts.
e.g. 4 years experience ≈ Bachelor's degree for technical roles · 2 years industry experience ≈ 1 year at target scale for scaling roles
Skill Substitution
Rule expressing that one skill can substitute for another in a given context without significant competency loss.
e.g. Python substitutes R in data science · React substitutes Vue/Angular/Svelte in frontend · AWS substitutes GCP/Azure in cloud
Skill Complement
Relationship indicating that two skills commonly appear together and mutually reinforce each other.
e.g. Python complements Airflow · SQL complements dbt · Kubernetes complements Docker
Skill Requires
Relationship indicating that proficiency in one skill presupposes competency in another.
e.g. dbt requires SQL · Airflow requires Python · Kubernetes requires Docker
SRO-to-CPO Match
Matching relationship between a Structured Requirement Object and a Candidate Profile Object producing a match score and gap analysis.
e.g. sro_abc123 matched to cpo_xyz789 produces match_score 0.92, essential_coverage 1.0, gap: no fintech experience
SRO-to-Question Generation
Derivation relationship from an SRO to interview questions, with questions grounded in specific SRO requirements.
e.g. SQL optimization question derived from sql proficiency 4 essential requirement in SRO
CPO-to-Question Personalization
Relationship where candidate-specific gaps and differentiating factors from CPO analysis customize the baseline question set.
e.g. Candidate A CPO gap in Airflow → generate targeted Airflow gap assessment question
Neighborhood-to-Agent Assignment
Assignment relationship mapping each semantic neighborhood to its dedicated specialist agent.
e.g. data_analytics neighborhood → data_specialist agent · software_engineering neighborhood → software_specialist agent

Constraints (6)

SRO Validation Rules
Structural constraints that must be satisfied before an SRO is considered valid for use.
e.g. Essential Skills ≤ 10 · Proficiency expectations consistent with role_level · Salary within market range
Essential Skills Cap (≤10)
No more than 10 essential skills may be specified in an SRO to avoid unrealistic wish-list job descriptions.
e.g. A JD with 15 essential skills would fail validation and trigger a reduction prompt
Proficiency Consistency
Proficiency expectations must be appropriate for the specified role level.
e.g. Entry-level role requiring Python proficiency 5 would fail this constraint
Experience Alignment
Required years of experience must match expectations for the specified role level.
e.g. Entry-level role requiring 7+ years would fail this constraint
Compensation Market Rate
Salary range must fall within accepted market range for the role, level, and location context.
e.g. $40k for a senior engineer in San Francisco would fail this constraint
Requirement Realism
Skill combinations specified must represent profiles that realistically exist in the candidate market.
e.g. Requiring 10 years of Kubernetes experience when it's only 10 years old would fail this constraint