Knowledge Graphs in Geode
Knowledge graphs represent structured information as interconnected entities and relationships, enabling semantic reasoning, intelligent search, and data integration. Geode’s native graph model provides an ideal foundation for building enterprise knowledge graphs that capture complex domain knowledge and relationships.
What is a Knowledge Graph?
A knowledge graph is a structured representation of real-world entities (people, places, concepts, events) and their relationships. Unlike traditional relational databases that store data in tables, knowledge graphs emphasize connections between data points, making it easier to discover hidden relationships and derive new insights.
Key characteristics:
- Entities: Real-world objects represented as nodes with properties
- Relationships: Typed connections between entities with optional properties
- Semantic meaning: Labels and relationship types convey domain semantics
- Schema flexibility: Easily evolve the graph structure as understanding grows
Building Knowledge Graphs with Geode
Creating entities:
-- Create entities with semantic labels
CREATE (company:Organization:Company {
name: 'Acme Corporation',
founded: 1985,
industry: 'Technology'
});
CREATE (person:Person:Employee {
name: 'Alice Smith',
title: 'Chief Technology Officer',
expertise: ['distributed systems', 'graph databases']
});
CREATE (concept:Concept:Technology {
name: 'Graph Database',
category: 'Data Management',
description: 'Database using graph structures for semantic queries'
});
Establishing relationships:
-- Connect entities with semantic relationships
MATCH (alice:Person {name: 'Alice Smith'}),
(acme:Company {name: 'Acme Corporation'})
CREATE (alice)-[:WORKS_FOR {since: 2018, role: 'CTO'}]->(acme);
MATCH (alice:Person {name: 'Alice Smith'}),
(graphdb:Technology {name: 'Graph Database'})
CREATE (alice)-[:HAS_EXPERTISE_IN {level: 'expert'}]->(graphdb);
Knowledge Graph Query Patterns
Traversing relationships:
-- Find all technologies a person has expertise in
MATCH (person:Person {name: 'Alice Smith'})-[:HAS_EXPERTISE_IN]->(tech:Technology)
RETURN tech.name, tech.category;
-- Find colleagues with shared expertise
MATCH (alice:Person {name: 'Alice Smith'})-[:HAS_EXPERTISE_IN]->(tech:Technology),
(colleague:Person)-[:HAS_EXPERTISE_IN]->(tech)
WHERE colleague <> alice
RETURN DISTINCT colleague.name, collect(tech.name) AS shared_expertise;
Path finding for knowledge discovery:
-- Discover connection paths between entities
MATCH path = shortestPath(
(company:Company {name: 'Acme'})-[*..6]-(target:Company {name: 'Competitor'})
)
RETURN path;
Use Cases
Enterprise knowledge management: Capture organizational knowledge, expertise, and relationships for better decision-making and knowledge discovery.
Semantic search: Enable intelligent search that understands context and relationships, not just keywords.
Recommendation systems: Leverage entity relationships for personalized recommendations based on user preferences and behavior patterns.
Data integration: Unify data from disparate sources into a coherent knowledge graph that reveals cross-domain insights.