Knowledge Graph Creation

Knowledge Graph Creation

This skill enables an AI agent to transform unstructured text into a structured knowledge graph. The agent extracts entities (people, organizations, technologies, concepts), identifies the relationships between them, generates formal graph triples (subject-predicate-object), and outputs the graph in both a queryable format (Cypher for Neo4j, JSON-LD) and a visual diagram (Mermaid). Knowledge graphs are valuable for understanding complex domains, powering semantic search, detecting implicit connections, and building recommendation systems.

Workflow

1. Analyze the Source Material: Read the input text and determine its domain, scope, and complexity. Identify the types of entities likely present (people, organizations, locations, technical concepts, events, etc.) and the granularity appropriate for the graph. A technical architecture document requires fine-grained component-level entities, while a news article may need coarser actor-level entities.

2. Extract Entities: Identify all named entities and significant concepts in the text. For each entity, record its canonical name, type (person, organization, technology, concept, event, location), and any notable attributes mentioned (e.g., founding date, version number, role). Deduplicate entities that appear under different names or abbreviations.

3. Map Relationships: For every pair of entities that interact in the text, identify the relationship between them. Express each relationship as a directed triple: (Subject) -[PREDICATE]-> (Object). Choose predicates from a consistent vocabulary (e.g., WORKS_AT, DEPENDS_ON, CREATED_BY, PART_OF, COMPETES_WITH). Record the source sentence for traceability.

4. Generate Graph Triples and Schema: Formalize the extracted data into a structured format. Output triples in one or more of: Cypher CREATE statements for Neo4j, JSON-LD for web interoperability, or a simple CSV of (subject, predicate, object) rows. Define a lightweight schema listing entity types and valid relationship types.

5. Visualize the Graph: Produce a human-readable visualization of the graph. Use Mermaid syntax for embedding in Markdown, or describe the layout for tools like D3.js, Gephi, or Neo4j Browser. Highlight central nodes and key relationship clusters.

6. Validate and Refine: Review the graph for completeness and accuracy. Check for orphan nodes (entities with no relationships), missing relationships implied by the text, and overly generic predicates that could be made more specific. Invite the user to confirm, correct, or request expansion of particular subgraphs.

Usage

Provide the agent with a text passage, document, or set of documents. Optionally specify the desired output format (Cypher, JSON-LD, Mermaid), the entity types of interest, or the maximum graph size.

Build a knowledge graph from the following text about our microservices architecture.
Output Cypher queries for Neo4j and a Mermaid diagram.

Text: "The API Gateway routes requests to the Auth Service and the Order Service.
The Order Service depends on the Inventory Service and the Payment Service.
The Payment Service integrates with Stripe and PayPal. All services publish
events to the Event Bus, which the Analytics Service consumes."

Examples

Example 1: Knowledge Graph from a Technical Document

User Request:

Build a knowledge graph from this text: "Kubernetes orchestrates containerized applications across clusters. A Pod is the smallest deployable unit in Kubernetes and contains one or more containers. Pods are managed by Deployments, which ensure the desired number of replicas. Services expose Pods to network traffic. Ingress routes external HTTP traffic to Services. etcd stores all cluster state data and is accessed by the API Server. kubectl is the CLI tool that communicates with the API Server."

Extracted Entities:

Relationships and Triples:

Cypher Queries:

CREATE (k:Technology {name: 'Kubernetes', description: 'Container orchestration platform'})
CREATE (pod:Concept {name: 'Pod', description: 'Smallest deployable unit'})
CREATE (container:Concept {name: 'Container', description: 'Runtime unit'})
CREATE (deploy:Concept {name: 'Deployment', description: 'Manages Pod replicas'})
CREATE (svc:Concept {name: 'Service', description: 'Network exposure layer'})
CREATE (ing:Concept {name: 'Ingress', description: 'External HTTP routing'})
CREATE (etcd:Technology {name: 'etcd', description: 'Distributed key-value store'})
CREATE (api:Component {name: 'API Server', description: 'Central management interface'})
CREATE (ctl:Tool {name: 'kubectl', description: 'CLI client'})

CREATE (k)-[:ORCHESTRATES]->(pod)
CREATE (pod)-[:CONTAINS]->(container)
CREATE (deploy)-[:MANAGES]->(pod)
CREATE (svc)-[:EXPOSES]->(pod)
CREATE (ing)-[:ROUTES_TO]->(svc)
CREATE (etcd)-[:STORES_STATE_FOR]->(k)
CREATE (api)-[:READS_FROM]->(etcd)
CREATE (ctl)-[:COMMUNICATES_WITH]->(api)