What are Knowledge Graphs? - Definition & Meaning
Learn what knowledge graphs are, how knowledge is stored as a network of entities and relations, and why they are powerful for search and AI.
Definition
A knowledge graph is a structured representation of knowledge as a network of entities (people, concepts, objects) and the relationships between them. It models facts as subject-predicate-object triples and enables rich queries.
Technical explanation
Knowledge graphs use graph models: nodes are entities, edges are relationships. Storage: graph databases (Neo4j, Amazon Neptune), RDF triplestores, or relational models. Schemas and ontologies (e.g., schema.org, custom taxonomies) define types and relations. Construction via NER, relation extraction, and manual curation. Use cases: search (Google Knowledge Graph), recommendation (product relations), RAG (context from graph), and enterprise search. Often combined with vector search for hybrid retrieval.
How AVARC Solutions applies this
AVARC Solutions builds knowledge graphs for clients with complex domain knowledge: product catalogs with relations, organization hierarchies, or subject-matter expertise. We combine knowledge graphs with RAG and semantic search for powerful AI solutions.
Practical examples
- A product knowledge graph where products, categories, brands, and "similar" relations are modeled for rich recommendations.
- A corporate portal where employees, projects, skills, and departments are stored as a graph for intelligent search and navigation logic.
- A medical knowledge network where conditions, medications, and symptoms are related for informed question-answering systems.
Related terms
Frequently asked questions
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