Concept Taxonomy

Twelve categories organize the 200 concepts in Learning Knowledge Graphs. No category exceeds 30% of the total (the largest is 13%), and each maps to one or more topics from the course description.

Category	TaxonomyID	Description	Concept Count
Graph Foundations	FOUND	Core graph vocabulary (node, edge, triple) and the relational/document-store paradigms it's contrasted against	26
Taxonomies and Ontologies	TAXO	Controlled vocabularies, thesauri, and formal ontology structures	16
Graph Data Models and Schema	MODEL	RDF triples vs. property graphs, and schema-level modeling (classes, properties, cardinality)	26
Semantic Web Standards	STD	RDF, RDFS, OWL, SKOS, SPARQL, URIs, and serialization formats	20
Building Knowledge Graphs	BUILD	ETL, entity extraction, entity resolution, and data cleaning for constructing a graph from source data	16
Querying Knowledge Graphs	QUERY	SPARQL and Cypher query languages, client libraries, and query patterns	16
Graph Algorithms and Analytics	ALGO	Centrality measures, traversal algorithms, and community detection	16
Knowledge Graphs and Machine Learning	GML	Embeddings, graph neural networks, and link prediction	16
Knowledge Graphs and LLMs	LLM	Large language models, retrieval-augmented generation, and GraphRAG	14
Cross-Industry Case Studies	CASE	Search, biomedical, recommendation, product, social, and other applied knowledge graphs	12
Legal and Enterprise Knowledge Graphs	LEGAL	Matter graphs, citation networks, expertise location, and conflicts checking	12
Governance and Maintenance	GOV	Data provenance, quality, versioning, and access control for keeping a graph current	10

Total: 200 concepts