Knowledge Graph Engineer
Knowledge Graph Engineers design and build graph-based knowledge representations that power search, recommendations, RAG pipelines, and enterprise knowledge management. Their daily English covers writing ontology design documents, explaining graph query results, presenting entity resolution strategies, and communicating the value of graph-based reasoning to product and data teams. This path covers the vocabulary of graph databases, ontologies, and structured knowledge systems.
Topics covered
- Ontology design
- RDF & SPARQL
- Property graphs
- Entity resolution
- Graph-based reasoning
- Knowledge graph for AI
Vocabulary spotlight
4 terms every Knowledge Graph Engineer should know in English:
A formal specification of concepts (classes), their properties, and the relationships between them — the schema layer of a knowledge graph
"We defined a product ontology with 12 classes and 45 properties before loading data into the knowledge graph."
The process of determining whether two records from different data sources refer to the same real-world entity — merging duplicates into a single canonical node
"Entity resolution merged 340,000 duplicate product records into 180,000 canonical entities with 97% precision."
The fundamental unit of RDF knowledge: a subject-predicate-object statement expressing a single fact (e.g., "Product X hasBrand Company Y")
"The knowledge graph contains 50 million triples representing product attributes and relationships."
Navigating a graph by following edges from node to node to find related entities or paths — the core operation for answering multi-hop questions
"A three-hop graph traversal finds all authors who have co-authored with someone who collaborated with the target researcher."
📚 Vocabulary Reference
Key terms organised by category for Knowledge Graph Engineers:
Graph Data Models
Ontology
Querying
Engineering
Recommended exercises
Real-world scenarios you'll practise
- Presenting a knowledge graph architecture to a product team: explaining how graph reasoning improves recommendation quality beyond what a relational database provides
- Writing an ontology design document: specifying the class hierarchy, property definitions, and cardinality constraints for a new domain
- Explaining entity resolution pipeline results to a data governance team: precision, recall trade-offs and their impact on data quality
- Justifying a knowledge graph for a RAG pipeline: explaining why structured graph retrieval outperforms dense vector search for multi-hop questions