Demystifying Skill Ontologies: Your Roadmap to Clarity

• Skills ontologies map skills, roles, relationships, and proficiencies using AI-powered intelligence, moving beyond static skill lists to create living, dynamic skill graphs.

• Organizations will leverage ontologies for real-time workforce insights, faster internal mobility, and equitable talent decisions with minimal manual effort.

• A robust ontology reduces reskilling time and accelerates promotions by identifying internal candidates and surfaces hidden skills across the organization.

• Implementation takes 3-6 months with the right framework, stakeholder alignment, and governance, starting with data hygiene and phasing across business units.

The way organizations manage talent is shifting. Instead of relying on job titles, credentials, and org charts, forward-thinking companies are moving toward skills-based decision-making. They match people to work based on what they can do, identify internal talent before going external, and reskill teams to fill emerging needs. But this shift requires one critical foundation: unified and accurate data about skills. Most organizations lack this structured skill data, while many more struggle with readily accessing and using it effectively. Skills are scattered across job descriptions, performance reviews, and learning records. They're inconsistent, outdated, and disconnected from roles and career paths.

A skills ontology solves this. It's the foundational data layer that maps skills, roles, and relationships using AI-powered intelligence, creating a living view of workforce capabilities. With it, you identify internal candidates faster, recommend targeted learning, and make talent decisions based on evidence.

In 2026, organizations that build strong ontologies gain a decisive edge. They leverage AI for workforce intelligence, automate talent insights, and make equitable decisions grounded in complete skill data. As labor markets tighten and reskilling becomes table stakes, this visibility separates organizations that thrive from those playing catch-up.

This guide explains what skills ontologies are, why they matter for talent acquisition and experience, what benefits they deliver, and how to build one.

In this Article:

What is a Skills Ontology?

A skills ontology is a collection of skills relationships that’s supported by a unified understanding of how strong or weak relationships between skills are. Simply put, this means a skills ontology is a collection of skills with knowledge of how each skill is related to each other within the context of your business. 

For example, a skills ontology can identify accounting as a skill and recommend related proficiencies and skills, such as Excel and data analysis, both of which might have strong relationships to the original accounting skill. 

But if an employee also cited writing as a skill alongside accounting, the ontology would understand that those two skills do not have a strong correlation to each other. This contextual understanding ensures that a skills ontology draws the right conclusions when pairing groups of skills together. 

For HR in particular, these enriched insights into skills, and more importantly, the relationship between them, create a robust understanding of a candidate's capabilities without manual effort.  It also allows for a more thorough understanding of what skills are required for each role — creating a detailed skills map that talent acquisition teams can use when identifying potential best-fit candidates for available positions, or managers can use when assessing gaps that might exist on their teams.

Related: What Every Company Needs to Know About Workforce Intelligence

Now that you have a better understanding of what a skills ontology is, let’s take a look at how it works to categorize skills and roles within an organization. 

What is a Skills Ontology?

A skills ontology works in two ways: 1) It determines the skills required for each role within an organization so managers know exactly what skills a candidate should possess to be successful in the role; 2) It assesses the skills existing employees possess, taking the manual work out of skills mapping so leaders can easily identify skills gaps across the business, as well as opportunities for growth and development. 

A skills ontology can understand three specific categories of data relationships: 

• Skill-to-skill relationships designate how each skill is related to each other and how strongly those skills are related. 

• Skill-to-role relationships highlight how skills are related to respective roles or job titles. 

• Role-to-role relationships focus on mapping how each role is related to other roles within an organization. 

However, not all skill ontologies are created to interpret all three relationship categories. Most skill ontologies solely focus on the skill-to-skill relationships or skill-to-role relationships whereas a robust skills ontology can take into account all relationship categories. 

Since most skill ontologies only focus on one or two areas, businesses often have to combine point solutions to first understand the skills data, then implement a tool that helps leaders take action based on that data set. 

For example, a standard HCM solely focuses on skill-to-skill relationships to recommend new skills based on the existing skills that each employee has. 

If a business only invests in a skill ontology platform with skill-to-skill relationships, they still need to designate time and resources to manually go through the skills data and associate it with respective roles. This can be a laborious task and still leaves companies scrambling to make sense of all the data available within a skill ontology. 

At Phenom, we train our skill ontologies to interpret and categorize all three relationships using proprietary platform data that we combine with any available data relevant to your company. This provides you with the necessary components to gain a holistic understanding of your workforce. 

Common Challenges in Skills Ontology Deployment

Organizations transitioning from title-based to skills-based hiring and workforce planning typically encounter friction points during ontology deployment. Rather than indicating failure, these challenges reflect the fundamental shift required in how organizations capture, structure, and apply workforce data. Understanding these common obstacles helps you plan for them, allocate appropriate resources, and build realistic timelines. Including:

• Data Quality Gaps: Most organizations discover skill data is messier than expected, with duplicate entries, inconsistent naming conventions, outdated descriptions, and shadow data scattered across spreadsheets and departmental databases. Before building an ontology, you need to consolidate, clean, and standardize this foundation.

• Governance and Ownership Confusion: Without clear ownership, skills ontologies become no one's responsibility, leading to stale data and reduced adoption. HR, L&D, IT, and business units all need defined roles and accountability.

• Change Management and Adoption Resistance: Moving teams from manual skill tracking to automated intelligence requires communication, training, and champion networks. Resistance often stems from misunderstanding the “why” or concerns about visibility.

• Integration Complexity: Connecting your ontology to existing systems (ATS, HRIS, LMS, labor-market APIs) takes planning. Incomplete integration means insights don't reach the people who need them.

AI/ML Inference Risks: Inaccurate skill tagging or biased recommendations without regular audits can undermine adoption and contradict DEI goals. Trust erodes quickly if the system makes unreliable suggestions.

What Makes Phenom’s Skill Ontologies Different?

Since most HCM’s only focus on skill-to-skill relationships, it can be difficult to layer in multiple-point solutions that work together to add more context without adding complexity to your systems.

Even if you use a third-party skills ontology with your HCM to get more insight into skills and roles, you still need to drive that information to the point of engagement with your candidates, employees, recruiters, and managers to make it actionable. 

To enhance the efficiency of skills ontologies while streamlining your experience, our development teams have taken a traditional skill ontology to new heights. 

1. Implementing custom-trained models and proprietary data specifically for HR 

Using custom-trained models and a proprietary set of data that we’ve been collecting for over a decade, our skills ontology takes into account all three relationships to create a deeper understanding of:

• The skills available within your workforce

• The skills needed to be successful in each role 

• The relationships between each skill 

• Which categories of skills should be attributed to each role

• How each role is related to the other roles 

• Where skills gaps exist based on existing knowledge

• What progressions are available to your organization for career pathing 

Our skills ontology, out of the box, has 50-60% of the necessary data to understand skills and role relationships within any organization in any industry. 

Related: AI and Skills Ontologies: Transforming Talent Management Across Industries

2. Adding another layer of context with your unique company data 

The next layer of knowledge comes from your unique company data for specific roles and skill sets that are needed.

To ensure our data is combined with pertinent company data, the Phenom skills ontology pulls relevant: 

• Employee data 

• Job titles and descriptions

• Organizational structures

• Learning materials 

• Proficiencies 

This combination of data allows the ontology to delineate similarities and differences between Phenom platform data and your specific company data to identify how closely related these similarities are. Throughout this process, the skill ontology is creating local groupings using critical company context without requiring manual work or data entry since it’s combined on top of out-of-the-box data sets filled with HR-specific data points. 

These additional attributes combined with existing platform data make up 90% of the ontology — accelerating time between implementation and adoption so you can start gaining a better understanding of your workforce faster. 

3. Validating combined skills data 

The next step in this process is focused on validating the available information to ensure that the skill ontology has the most accurate skills, career progressions, and roles in alignment with your existing workforce. We work closely with your talent management team to annotate the information before publishing the skill ontology. If you’re unsure about how all of this comes together differently than a traditional skill ontology or HCM, here’s an analogy that highlights the differences: 

If a career architecture was a destination you had to drive to, point solutions might provide you with an engine without the car or a car without the navigational system. Phenom gives you the engine, the car, the ability to navigate, and things to do when you get to your destination. 

You might be thinking, that sounds great, but how does all of this help create better talent experiences? Let’s dive into the benefits you can experience from implementing a robust skill ontology at your organization. 

Related: Bringing Employee Experiences to Life: How GE Plans, Engages, and Retains with Workforce Intelligence

Benefits of Skills Ontologies

Skill ontologies are designed to make parsing skills data simple. They also help support a larger ecosystem of HR tools that drive actionable insights throughout the Phenom platform. 

Pairing insights with action is a winning combination that ensures the skills data that you have access to is distilled into bite-sized recommendations that help you get from where you are to where you want to be. 

Numerous benefits are directly linked to implementing a robust skills ontology. Let’s take a look at four of them: 

1. Elevated workforce intelligence for data-driven decision-making

Having access to the data is not enough. With workforce intelligence tools, you can access comprehensive datasets while also getting actionable insights and recommendations based on the data itself. This is a critical step that brings skill ontology implementation full circle. 

Out-of-the-box data combined with company data ensures that the Phenom skills ontology creates a comprehensive understanding of the skills available within your organization. With a universal understanding of skill-to-skill, skill-to-role, and role-to-role relationships, skill ontologies power workforce intelligence insights that help HR leaders get a clear picture of the entire workforce.

This deeper understanding of an organization’s workforce helps leaders make evidence-based policy development, as well as strategic planning and forecasting possible, since the information available within a skill ontology is up-to-date and captures any changes within the workforce in real time. 

Having access to accurate, comprehensive, and evolving information is critical when leaders are making decisions to drive the direction of the business forward. 

For employees, well-developed skills ontologies help enhance existing skills information while identifying any areas of concern that need to be addressed. Instead of solely relying on employee input related to their skills, skill ontologies apply additional context to employee profiles, creating a robust understanding of their skills, preferences, interests, and expertise. 

This enhanced information drives more relevant career path recommendations across the enterprise to connect individuals with potential career moves that may not have otherwise been considered.

Related: Future-Proofing Your Workforce by Mastering Skill and Competency Gaps

These are just a few of the ways in which skills ontologies create foundations of knowledge that power data-driven decision-making for key stakeholders. 

2. Enhanced education and training 

Understanding what skills are available — paired with visible career pathing journeys for each individual employee — managers can help guide talent development through recommended courses of action. This can include mentorship opportunities, gigs and short-term projects, or coursework aimed at helping employees reach their next career milestone while staying in alignment with greater company goals. 

For example, if leadership sees an opportunity to upskill an employee in preparation for a role that will become available in the future, they can support tailored learning programs in an effort to empower internal mobility while ensuring TA teams can prioritize finding external talent for roles that don’t have a clear succession plan. 

This approach offers improved visibility and collaboration between talent acquisition and talent management departments, effectively promoting efficient resource allocation. 

3. Streamlined workforce planning processes

Skill ontologies go beyond outlining skills available within your organization — they allow for the rapid deployment of career architectures, creating clear paths for your employees to follow so they can thrive within your company, today and in the future. 

By clearly outlining the skills each employee possesses, leadership can strategically bridge skills gaps, ensuring your employees are well-equipped to take on new challenges. This also surfaces any areas that require external talent to fill skill gaps. This level of insight can inform TA teams and empower them to make more targeted hiring efforts that are honed around acquiring specific skills. 

Pairing a targeted approach with the right technology can cut down on sourcing and screening time, allowing TA teams to make more meaningful connections with best-fit candidates, faster. 

4. Delivering better experiences for employees and recruiters

A robust skills ontology unlocks invaluable insights for HR leaders, but it also provides value for employees and recruiters. 

For employees, well-developed skills ontologies help enhance existing skills information while identifying any areas of concern that need to be addressed. Instead of solely relying on employee input related to their skills, skill ontologies apply additional context to employee profiles, creating a robust understanding of their skills, preferences, interests, and expertise. 

This enhanced information drives more relevant career path recommendations across the enterprise to connect individuals with potential career moves that may not have otherwise been considered.

For recruiters, skills ontologies highlight critical gaps that need to be addressed alongside skills that recruiters should look for in potential candidates. This informed approach to recruiting offers a more targeted and mindful approach, ensuring that recruiters connect with best-fit candidates that align with the job responsibilities as well as the overarching business goals. 

It also expands the talent pool by surfacing potential internal candidates that can be upskilled and promoted into a different role — accelerating time to hire since the candidate is already an employee. 

Overall, a skills ontology helps make more contextualized connections between critical pieces of information, powering more engaging and personalized experiences for users throughout the organization. 

Future Trends: 2026 and Beyond

Skills ontologies are evolving rapidly. Organizations building them today should plan for these shifts.

• Real-Time Skills Graphs and Dynamic Role-Matching: Static role definitions are becoming obsolete. Applications of ontologies will continuously match employees to opportunities, not just at promotion time, but weekly or monthly as roles, teams, and skills needs shift. This requires tighter integration with workforce planning and project management systems.

• Micro-Credentials and Skill Badges: As employees accumulate industry-backed certifications, course completions, and learning badges, ontologies will integrate these as verifiable skill signals. Rather than relying on broad job titles as the primary measure of capability, organizations gain a more nuanced understanding of what employees can actually do. 

• Gig, Project-Based, and Internal Talent Marketplaces: As work becomes more fluid (gig economy, internal project staffing, secondments), ontologies will map skills not to static roles, but to actual project needs. This flexibility will be table stakes for competitive organizations.

• AI/LLM-Driven Skill Inference and Automation: Large language models will accelerate skill extraction from unstructured data (performance reviews, emails, project outcomes) and discover non-obvious relationships between skills. Anomaly detection will flag when skills aren't being recognized or when skill gaps emerge faster than expected.

• External Benchmarking and Labor-Market Integration: Ontologies will pull real-time labor-market data, showing what skills are in demand, what they command in salary, and how your skills inventory compares to those of competitors and the industry. This enables proactive workforce planning instead of reactive hiring.

• Federated and Cross-Organizational Governance: In industries with tight labor markets and talent consortiums, ontologies may be shared across organizations with individual customization layers. This pooled approach reduces duplication and speeds skill standardization.

• Responsible AI and Explainability: As regulatory scrutiny increases (DEI, fair hiring, data privacy), ontology systems will need built-in transparency: why was a skill recommended? Which data fed into this decision? How do we audit for bias? Explainability becomes a product differentiator.

Frequently Asked Questions on Skills Ontologies

1. What is a skills ontology? How is it different from a taxonomy?

A skills ontology is a structured, relationship-aware map of skills, roles, and proficiencies, understanding how skills connect to specific roles. A taxonomy is a simpler hierarchical list (like a folder structure). An ontology is far more powerful because it captures context and relationships, enabling smarter matching, recommendations, and gap analysis.

2. What is a skills-based approach to hiring and workforce planning?

A skills-based approach shifts decision-making from job titles and tenure to actual capabilities. In workforce planning, it means identifying internal talent for new roles, recommending reskilling paths, and building teams around needed capabilities rather than predetermined structures. 

3. How often should a skills ontology be updated?

Quarterly, at a minimum, for new skills, mapping adjustments, and external labor-market change.s Semi-annually for AI model refreshes and bias audits. Annually for comprehensive reviews. Event-driven updates should happen whenever roles are restructured or major business changes occur.

4. Who should own the skills ontology?

Use a shared governance model. Assign one owner (usually HR or L&D leader) for overall accountability, a data steward (HR or IT) for data quality, an AI/ML owner for model accuracy, and business unit champions for validation and adoption. A RACI matrix prevents confusion.

5. Can ontologies help achieve DEI goals?

Yes, if designed with equity in mind. Include non-traditional skills (gig work, volunteering, certifications), audit for naming bias, and measure whether recommendations and mobility opportunities reach all demographics equally. Without deliberate design, an ontology can codify existing biases.

6. How does AI impact skill inference and governance?

AI automates skill extraction from job descriptions and employee profiles, discovers relationships without manual mapping, and enables real-time updates. However, it introduces bias risks. Models must be audited regularly, explainability is critical, and human validation is non-negotiable.

7. How do you avoid bias in skills mapping?

Use diverse language in skill naming (avoid gendered or culturally specific terms), include soft and latent skills (not just technical ones), apply consistent mapping rules across all roles, audit AI recommendations for fairness, and regularly review whether skill opportunities and mobility reach all employee groups equally.

Next Steps for Implementing a Skills Ontology

Skills ontologies are revolutionizing the HR landscape by providing a powerful tool for improved workforce intelligence. To get a better view of your organization’s roles, skills, and progressions, sign up for a personalized Skills Snapshot. 

Once you fill out the form and meet with our team of experts, you’ll receive a snapshot of your company’s skills, plus tailored recommendations that will help you take the right steps toward becoming a skills-forward organization. 

Ready to turn workforce data into action? Get the guide that shows you how to deploy and adopt a skills-based approach.