Ontology

The graph is built on two node types: Topics and Skills. Together they form a rich, hierarchical taxonomy of knowledge that the rest of the platform connects to.

Topics and Skills

Topics

Hierarchical knowledge domains

Topics form a taxonomy of human knowledge — from broad fields like “Computer Science” down to specific concepts like “Transformer architecture”. They nest via SUBTOPIC_OF to arbitrary depth.

Topic Types

Field

Subfield

Concept

Method

Technique

Instrument

Genre

Era

Theory

Practice

Tool

Framework

Person

Other

Skills

Abilities and competencies

Skills represent concrete abilities — things a learner can actually do after engaging with content. Every skill relates to at least one Topic viaSKILL_IN_TOPIC.

Technical

Hard skills tied to specific tools, languages, or processes

Soft

Interpersonal and cognitive abilities that transfer across contexts

Psychomotor

Physical skills requiring coordinated body movements

Node Sources

Where Topics and Skills come from

ESCO

European Skills, Competences, Qualifications and Occupations taxonomy — a standardised EU-level skill ontology covering thousands of occupations and skills.

AI-Generated

Topics and skills surfaced by the Librarian agent during cataloging. Reviewed and disambiguated against existing nodes before being committed.

Manual

Nodes created or linked directly by educators, administrators, or learners. The highest-trust source, always honoured for recommendations.

Topics form a taxonomy of human knowledge — from broad fields like Computer Science down to specific concepts like Transformer architecture. Skills represent concrete abilities — things a learner can actually do after engaging with content. Every skill is anchored to at least one Topic via SKILL_IN_TOPIC, so the graph can reason across both domains.

Multilingual by Design

All Topics and Skills are available in six languages: English, German, Spanish, French, Dutch, and Portuguese. The graph stores names and descriptions for each locale, so users always see the graph in their own language.

Relationships Within the Ontology

Topics and Skills are not flat lists — they form a rich internal structure:

Relationship	Direction	Meaning
`SUBTOPIC_OF`	Topic → Topic	Builds a hierarchy from broad fields down to specific concepts
`SUBSKILL_OF`	Skill → Skill	Creates skill hierarchies, grouping related competencies
`SKILL_IN_TOPIC`	Skill → Topic	Anchors skills to their domain, enabling cross-domain discovery
`RELATED_ESSENTIAL_TOPIC` / `RELATED_ESSENTIAL_SKILL`	Topic/Skill → Topic/Skill	Strong lateral connections between related areas
`RELATED_OPTIONAL_TOPIC` / `RELATED_OPTIONAL_SKILL`	Topic/Skill → Topic/Skill	Weaker lateral links for broader discovery

Recommendation and discovery logic use these relationships to traverse the graph — for example, finding resources in child topics via SUBTOPIC_OF or in related domains via RELATED_ESSENTIAL_TOPIC.

Where Nodes Come From

The ontology is seeded from ESCO (European Skills, Competences, Qualifications and Occupations), a validated EU taxonomy. New nodes are added when the Librarian agent proposes them during cataloging (after deduplication) or when educators create them manually. Manual and AI-generated nodes are disambiguated against existing ones to avoid fragmentation.

Next Steps

Entity Connections — how platform entities link to Topics and Skills
Signals & Sources — how each link is labelled and weighted
Exploration — browse and search the ontology in the UI