ontology-engineer

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

1. Identify the user's ontology need (design, taxonomy, querying, or knowledge graph)
2. Follow the corresponding workflow below
3. 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:

1. 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?"

2. Reuse assessment

   • Search existing ontologies (BioPortal, LOV, OntoBee)
   • Import and align relevant upper ontologies (DOLCE, BFO, schema.org)
   • Document reuse decisions and mappings

3. Conceptual modeling

   • Identify entities (classes), relationships (properties), instances
   • Create class hierarchy (is-a relations)
   • Define object properties (relations between classes) and data properties (attributes)

4. 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

5. Validation & reasoning

   • Check consistency with reasoner (HermiT, Pellet, FaCT++)
   • Verify competency questions with SPARQL
   • Review with domain experts

2. Knowledge Graph Construction

Construction pipeline:

1. Source identification

   • Structured: relational databases, APIs, CSV
   • Semi-structured: JSON, XML, logs
   • Unstructured: text, documents, images

2. Schema/ontology alignment

   • Map source schemas to ontology
   • Handle property mapping, unit conversion, URI generation

3. 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)

4. 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