A knowledge graph is a structured representation of real-world entities and the relationships between them, stored as a network of nodes and edges. Each node represents an entity — a person, product, organisation, concept, or event — and each edge represents a named, directed relationship between two entities. Knowledge graphs enable AI systems to reason about complex, multi-hop relationships that tabular data cannot capture, supporting richer search, recommendation, and question-answering applications.
A knowledge graph represents information as a collection of triples: subject — predicate — object. For example: “AINinza” — “is headquartered in” — “Noida”. Or: “RAG” — “is a type of” — “retrieval system”. These triples are stored in a graph database and can be traversed at query time to answer questions that require following chains of relationships across multiple entities.
Nodes
Entities: people, products, organisations, concepts
Edges
Named relationships between entities: “works at”, “is part of”, “caused by”
Properties
Attributes on nodes and edges: timestamps, confidence scores, source provenance
The power of a knowledge graph comes from traversal. A query can ask: “Find all suppliers connected to a given raw material through at most three relationship hops” — a question that requires expensive multi-table joins in a relational database but is a simple graph traversal. Graph databases like Neo4j, Amazon Neptune, and Azure Cosmos DB (Gremlin API) are optimised for this traversal pattern at scale.
Enterprise knowledge graphs are typically built from multiple data sources: structured databases, document repositories, APIs, and external knowledge bases. Natural language processing models extract entities and relationships from unstructured text — emails, reports, contracts — and add them to the graph. The graph becomes a unified, relationship-aware representation of everything the organisation knows.
Standard RAG (Retrieval-Augmented Generation) retrieves text chunks from a vector database and passes them as context to an LLM. This works well for factual questions answered by a single document, but struggles with questions that require reasoning across multiple entities and their relationships. GraphRAG addresses this by replacing or augmenting vector retrieval with knowledge graph traversal.
When a user asks “Which of our suppliers share a director with a company that has had regulatory sanctions in the past three years?” — a vector database cannot answer this. A knowledge graph that encodes supplier-director-company-sanction relationships can answer it precisely, and the LLM uses the structured graph context to generate a human-readable response with full entity references.
AINinza builds GraphRAG systems that combine both approaches: vector retrieval for semantic similarity over unstructured content, and knowledge graph traversal for relationship-aware factual queries. The retrieval layer selects the best approach based on query type, passing structured graph context or unstructured document chunks to the LLM as appropriate.
Fraud rings share entities — addresses, phone numbers, IP addresses, device fingerprints — across multiple apparently independent accounts. A knowledge graph connecting all account entities through shared attributes exposes these networks in seconds, enabling investigators to act before the fraud is completed. Graph neural network (GNN) models trained on the entity relationship structure achieve significantly higher fraud detection rates than tabular models that treat each account in isolation.
A product knowledge graph encodes entities (products, brands, categories, attributes, customer segments) and relationships (compatible with, frequently bought with, supersedes, belongs to). Search and recommendation engines built on this graph understand that a customer searching for “running shoes for flat feet” should see shoes with specific arch support attributes — not just products with those keywords in the title.
IT service management knowledge graphs map dependencies between applications, services, infrastructure components, and business processes. When an alert fires, the graph immediately shows which upstream services depend on the affected component and which business processes are at risk — dramatically reducing mean time-to-impact-assessment and helping operations teams prioritise incident response correctly.
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