Knowledge Network Builder Suite

Intelligent knowledge graph and network building toolkit for creating interconnected knowledge bases, concept maps, entity relationship graphs, and semantic networks from unstructured data.

When to Use This Skill

Choose Knowledge Network Builder when:

• Building interconnected knowledge bases from documents and data
• Creating concept maps that visualize relationships between ideas
• Extracting entities and relationships from unstructured text
• Building recommendation systems based on content relationships
• Creating internal wikis with intelligent cross-referencing

Consider alternatives when:

• Need a simple document store — use a wiki or CMS
• Need graph database queries — use Neo4j directly
• Need full-text search — use Elasticsearch or Algolia

Quick Start

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# Activate knowledge network builder
claude skill activate rapid-knowledge-network-builder-suite

# Build knowledge graph
claude "Build a knowledge graph from our documentation in /docs/"

Example: Knowledge Graph Construction

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interface KnowledgeNode {
  id: string;
  type: 'concept' | 'entity' | 'document' | 'topic';
  label: string;
  properties: Record<string, any>;
  embedding?: number[];  // Vector for semantic search
}

interface KnowledgeEdge {
  source: string;
  target: string;
  relationship: string;  // "relates_to", "part_of", "depends_on"
  weight: number;         // 0-1 relationship strength
  bidirectional: boolean;
}

interface KnowledgeGraph {
  nodes: Map<string, KnowledgeNode>;
  edges: KnowledgeEdge[];

  addNode(node: KnowledgeNode): void;
  addEdge(edge: KnowledgeEdge): void;
  findRelated(nodeId: string, depth?: number): KnowledgeNode[];
  search(query: string): KnowledgeNode[];
  shortestPath(from: string, to: string): KnowledgeNode[];
  getSubgraph(nodeIds: string[]): KnowledgeGraph;
}

// Build graph from documents
async function buildFromDocuments(docs: Document[]): Promise<KnowledgeGraph> {
  const graph = new KnowledgeGraph();

  for (const doc of docs) {
    // Extract entities
    const entities = await extractEntities(doc.content);
    for (const entity of entities) {
      graph.addNode({
        id: entity.id,
        type: 'entity',
        label: entity.name,
        properties: { source: doc.id, type: entity.type },
      });
    }

    // Extract relationships
    const relationships = await extractRelationships(doc.content, entities);
    for (const rel of relationships) {
      graph.addEdge({
        source: rel.from,
        target: rel.to,
        relationship: rel.type,
        weight: rel.confidence,
        bidirectional: rel.type === 'relates_to',
      });
    }
  }

  return graph;
}

Core Concepts

Graph Components

Component	Description	Example
Nodes	Entities, concepts, documents	"React", "Virtual DOM", "ComponentLifecycle.md"
Edges	Relationships between nodes	"React" --uses--> "Virtual DOM"
Properties	Metadata on nodes and edges	{ category: "framework", version: "18" }
Clusters	Groups of related nodes	"Frontend Technologies" cluster
Paths	Connections between distant nodes	React → Virtual DOM → Reconciliation → Fiber

Knowledge Extraction Methods

Method	Input	Output	Tool
NER	Unstructured text	Named entities	spaCy, OpenAI
Relation Extraction	Text + entities	Entity relationships	LLM, dependency parsing
Topic Modeling	Document corpus	Topic clusters	LDA, BERTopic
Embedding Similarity	Content	Semantic connections	Sentence transformers
Link Analysis	Documents with references	Citation graph	Custom parsing
Ontology Mapping	Domain knowledge	Hierarchical structure	Expert curation

Configuration

Parameter	Description	Default
graph_backend	Storage: neo4j, networkx, in-memory	in-memory
extraction_model	NER/RE model for entity extraction	gpt-4
embedding_model	Model for semantic embeddings	text-embedding-3-small
similarity_threshold	Min similarity for automatic linking	0.75
max_depth	Maximum traversal depth for queries	3
visualization	Viz library: d3, cytoscape, mermaid	d3

Best Practices

1. Define a clear ontology before building the graph — Establish node types, relationship types, and naming conventions upfront. A well-defined schema prevents inconsistency as the graph grows and makes querying predictable.

2. Use embeddings for discovering implicit relationships — Not all connections are explicit in text. Compute vector embeddings for node descriptions and connect nodes whose embeddings exceed a similarity threshold. This surfaces related concepts that aren't directly cross-referenced.

3. Implement incremental updates, not full rebuilds — As new documents arrive, extract entities and relationships incrementally. Match new entities against existing nodes before creating duplicates. Use entity resolution to merge different names for the same concept.

4. Visualize subgraphs, not the entire network — Full network visualization is overwhelming for graphs over 50 nodes. Show local neighborhoods (2-3 hops from a selected node) or topic clusters. Use interactive exploration rather than static full-graph views.

5. Validate automatically extracted relationships with human review — Automated extraction produces false positives. Implement a review workflow where extracted relationships are flagged for confirmation, especially for high-impact connections that affect recommendations or navigation.

Common Issues

Entity resolution fails — same concept appears as multiple nodes. Implement fuzzy matching on entity names, acronym expansion, and embedding-based similarity for deduplication. Maintain an alias table mapping variations to canonical names. Review and merge duplicates regularly.

Graph becomes too dense to navigate or query efficiently. Prune low-weight edges, remove isolated nodes, and collapse tightly connected clusters into summary nodes. Use community detection algorithms to identify natural groupings and provide hierarchical navigation.

Knowledge graph becomes stale as source documents change. Implement a refresh pipeline that re-processes updated documents and reconciles changes with the existing graph. Track document modification timestamps and prioritize re-extraction for recently changed sources.