Knowledge Graph

A knowledge graph is a structured representation of entities (people, places, things, concepts) and their relationships, organized as a graph where nodes are entities and edges are connections. Knowledge graphs enable reasoning, traversal, and synthesis by capturing explicit relationships like "Alice works at Acme" or "Project Alpha depends on Task 123."

Unlike unstructured text or flat databases, knowledge graphs support multi-hop reasoning and relationship queries: "Who works at companies using our product?" or "Which tasks block Project X?" This structure is foundational for agent memory, semantic search, personalization, and contextual AI applications.

The outcome is queryable, connected knowledge that agents and systems use for intelligent reasoning, not just keyword matching or similarity search.

Why it matters

• Enables relationship reasoning: Agents can answer "who owns what?" or "what depends on this?" by traversing the graph—not just matching keywords.
• Powers semantic search and RAG: Entity-filtered search ("show documents about Project Alpha") is more precise than keyword or embedding similarity alone.
• Supports personalization: Understanding "Alice prefers async updates" or "Bob is expert in authentication" enables context-aware interactions.
• Facilitates multi-hop queries: Agents can follow chains: "Alice works at Acme, Acme uses Graphlit, therefore Alice might know about Graphlit."
• Reduces hallucinations: Grounding agent responses in verified graph relationships prevents plausible-but-wrong answers.
• Improves data integration: Knowledge graphs unify entities across systems—"Alice Johnson" in Slack, "ajohnson" in Jira, and "alice@acme.com" in email resolve to one entity.

How it works

Knowledge graphs are built through extraction, linking, and storage:

• Ingestion → Content (documents, messages, structured data) flows into the system.
• Entity Extraction → NLP models identify entities: people (Alice Johnson), companies (Acme Corp), projects (Alpha), tasks (Task 123), places (San Francisco).
• Relationship Extraction → The system identifies connections: "Alice works at Acme," "Task 123 assigned to Alice," "Alpha depends on Beta."
• Entity Linking → Multiple references to the same entity ("Alice Johnson," "Alice J.," "ajohnson@acme.com") are resolved to a single canonical node.
• Graph Storage → Entities and relationships are stored in a graph database optimized for traversal: Neo4j, Amazon Neptune, or custom graph stores.
• Graph Queries → Agents and applications query the graph using patterns: "Find all tasks owned by people at Acme who are blocked on authentication."

This pipeline transforms unstructured content into a queryable knowledge network.

Comparison & confusion to avoid

Examples & uses

Team knowledge graph
Entities: Alice (Person), Acme (Company), Project Alpha (Project), Task 123 (Task). Relationships: Alice works_at Acme, Alice owns Task 123, Task 123 blocks Alpha, Alpha depends_on Beta. Query: "What tasks owned by Acme employees block Alpha?" Traversal: Acme → employs → Alice → owns → Task 123 → blocks → Alpha.

Customer relationship graph
Entities: CustomerCo (Company), IssueX (Support Ticket), FeatureY (Product Feature), EngineerZ (Person). Relationships: CustomerCo reported IssueX, IssueX relates_to FeatureY, FeatureY owned_by EngineerZ. Query: "Which engineers should we notify about CustomerCo issues?" Traversal: CustomerCo → reported → IssueX → relates_to → FeatureY → owned_by → EngineerZ.

Research citation graph
Entities: PaperA (Document), AuthorX (Person), ConceptY (Topic). Relationships: PaperA cites PaperB, AuthorX wrote PaperA, PaperA discusses ConceptY. Query: "Who are the most cited authors in ConceptY?" Traversal: ConceptY → discussed_in → Papers → written_by → Authors, ranked by citation count.

Best practices

• Extract relationships explicitly: Don't assume embeddings capture relationships—use NLP or structured extraction to identify "Alice works at Acme."
• Canonicalize entities with linking: Ensure "Alice Johnson," "Alice J.," and "ajohnson@acme.com" resolve to one node—fragmentation breaks reasoning.
• Use typed relationships: "owns," "depends_on," "mentions" have different meanings—typed edges enable precise queries.
• Model bidirectional relationships: If "Alice works_at Acme," also store "Acme employs Alice"—supports queries from either direction.
• Combine with vector search: Knowledge graphs excel at structure; vector search excels at semantic similarity—hybrid approaches are powerful.
• Track entity provenance: Store where entities came from (which document, timestamp)—enables confidence scoring and debugging.

Common pitfalls

• Confusing graphs with embeddings: Embeddings capture similarity, not explicit relationships—you need both for robust agent memory.
• No entity linking: If every mention creates a new node, the graph fragments—"Project Alpha" and "Alpha Project" should be one entity.
• Over-extraction of entities: Extracting every noun creates noise—focus on salient entities (people, companies, projects) for reasoning.
• Ignoring temporal aspects: Relationships change—"Alice worked at Acme from 2020-2023"—combine knowledge graphs with temporal memory.
• Static graphs: Entities and relationships evolve—implement update and versioning strategies.

See also

• Semantic Memory — Meaning-based memory stored in knowledge graphs
• Entity Linking — Resolving multiple references to canonical identities
• Fact Extraction — Extracting structured facts from unstructured content
• Agent Memory — Persistent context powered by knowledge graphs
• Semantic Retrieval — Fetching context based on meaning and relationships

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See how Graphlit implements Knowledge Graphs for agent memory → Agent Memory Platform