daemon-blockint-tech Ontology Engineer
Overview
Design ontologies and build knowledge graphs. This skill covers OWL/RDF ontologies, SKOS taxonomies, SPARQL querying, knowledge graph construction, semantic reasoning, and linked data patterns.
Features
- OWL/RDF ontology design: classes, properties, restrictions, axioms
- SKOS taxonomy creation: concepts, hierarchies, labels, mappings
- SPARQL querying: SELECT, CONSTRUCT, ASK, DESCRIBE patterns
- Knowledge graph construction: data extraction, entity resolution, graph loading
- Semantic reasoning: rule-based inference, OWL reasoning, consistency checking
- Linked data patterns: URIs, dereferencing, RDF serialization, data publishing
Usage
- Identify the user's ontology need (design, taxonomy, querying, or knowledge graph)
- Follow the corresponding workflow below
- Produce structured outputs: OWL files, SKOS taxonomies, SPARQL queries, or knowledge graph schemas
Examples
User: "Design an ontology for products" Agent: Runs Ontology Design workflow, defines classes (Product, Category, Feature), properties (hasCategory, hasFeature), produces OWL file
User: "Write a SPARQL query" Agent: Runs Querying workflow, constructs SELECT query with graph patterns, filters, and aggregations
User: "Build a knowledge graph" Agent: Runs Knowledge Graph Construction workflow, extracts entities, resolves duplicates, loads into triple store
When to Use
- Scoping domains with competency questions and designing OWL/RDF ontologies
- Building knowledge graphs, entity resolution, and linked-data integration
- Writing SPARQL, Cypher, or graph validation and reasoning workflows
- Selecting semantic-web or property-graph tools and reuse from public ontologies
When NOT to Use
- Relational warehouse star schemas or batch ETL → use
data-warehouse-engineer - Enterprise data platform vendor selection or mesh operating model → use
data-architect - LLM system prompts, agents, or RAG orchestration → use
prompt-engineer - Business requirements workshops without semantic modeling → use
business-analyst
Core Workflows
1. Ontology Design Workflow
Phase checklist:
Scope & competency questions
- Define the domain boundaries
- Write 5-10 competency questions the ontology must answer
- Example: "Which drugs interact with proteins encoded by a given gene?"
Reuse assessment
- Search existing ontologies (BioPortal, LOV, OntoBee)
- Import and align relevant upper ontologies (DOLCE, BFO, schema.org)
- Document reuse decisions and mappings
Conceptual modeling
- Identify entities (classes), relationships (properties), instances
- Create class hierarchy (is-a relations)
- Define object properties (relations between classes) and data properties (attributes)
Formalization in OWL/RDF
- Encode in OWL 2 (DL, RL, or QL profile based on reasoning needs)
- Add restrictions (cardinality, value constraints)
- Define inverse, transitive, symmetric properties
Validation & reasoning
- Check consistency with reasoner (HermiT, Pellet, FaCT++)
- Verify competency questions with SPARQL
- Review with domain experts
2. Knowledge Graph Construction
Construction pipeline:
Source identification
- Structured: relational databases, APIs, CSV
- Semi-structured: JSON, XML, logs
- Unstructured: text, documents, images
Schema/ontology alignment
- Map source schemas to ontology
- Handle property mapping, unit conversion, URI generation
Entity extraction & resolution
- Extract entities from unstructured sources (NER, RE)
- Resolve duplicates: "IBM" = "International Business Machines" = "IBM Corp."
- Link to external identifiers (Wikidata, DBpedia, ORCID)
Graph population
- Transform to RDF triples or property graph format
- Load into triple store or graph database
- Validate graph completeness and quality
3. Querying & Retrieval
Choose query language by store type:
| Store Type | Query Language | Use Case |
|---|---|---|
| RDF triple store | SPARQL | Semantic web, OWL reasoning, linked data |
| Labeled property graph | Cypher | Neo4j, pattern matching, path queries |
| GraphQL | GraphQL+ | API-layer graph queries |
| Gremlin | Gremlin | Traversal-heavy, multi-model graphs |
4. Validation & Reasoning
Reasoning tasks:
- Consistency checking: No contradictory class assertions
- Classification: Infer subclass hierarchies
- Property entailment: Infer transitive, inverse, symmetric relations
- Instance checking: Validate type assertions
Validation checklist:
- Ontology is consistent (no unsatisfiable classes)
- All competency questions answerable with queries
- No orphan classes or properties
- URIs are dereferenceable or resolvable
- Labels and descriptions in multiple languages if needed