Verified:GitHub

Knowledge Graph Builder

Build knowledge graphs with ontology design and entity relationship extraction.

7|3|Updated Jan 15, 2026

Search Tags:#ontology #RAG #knowledge-graph #semantic-search #graph-database #hallucination-detection #entity-resolution

Ready to run

npx skills add https://github.com/Zpankz/mcp-skillset --skill knowledge-graph-builder-zpankz

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Knowledge Graph Builder
Source: https://github.com/Zpankz/mcp-skillset/tree/main/knowledge-graph-builder
Command: npx skills add https://github.com/Zpankz/mcp-skillset --skill knowledge-graph-builder-zpankz

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Knowledge graphs help teams model complex entity relationships, ground AI outputs, and enable robust reasoning over connected data to prevent hallucinations.

Core Features & Use Cases

Ontology design: define entities, relationships, and properties to capture domain concepts.
Graph database selection and schema: choose appropriate storage and querying strategies, plus data governance.
Entity and relationship extraction: automate discovery and validation against ontology, with confidence scoring.
Hybrid KG+RAG integration: combine structured graph traversal with vector search for semantic retrieval.
AI grounding and verification: validate claims against graph evidence and detect inconsistencies.
Queryable APIs: expose common patterns like find entity, find relationships, and shortest paths.

Quick Start

Define a minimal ontology for your domain and bootstrap a Neo4j graph with a few entities and relationships to test queries.

Frequently Asked Questions about Knowledge Graph Builder

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build a knowledge graph to ground AI outputs and prevent hallucinations?▼

To build a knowledge graph for AI grounding, define a domain ontology, extract entities and relationships with confidence scoring, and validate claims against the graph evidence to detect inconsistencies and prevent hallucinations.

What is hybrid KG+RAG integration for semantic search?▼

Hybrid KG+RAG integration combines structured graph traversal with vector search to enable semantic retrieval. This approach validates retrieved relationships against an ontology to provide confidence-scored evidence for AI reasoning.

How do I design an ontology and extract entities for a knowledge graph?▼

Design an ontology by defining domain entities, relationships, and properties. Automate entity and relationship extraction to discover graph nodes, validating them against the ontology schema and assigning confidence scores to mapped relationships.

What's the best way to choose a graph database and query complex entity relationships?▼

Choose a graph database by evaluating storage and querying strategies for your domain schema. You can query complex entity relationships by exposing APIs to find entities, map relationships, and calculate shortest paths across the graph.

Do I need Neo4j to bootstrap a knowledge graph and test relationship queries?▼

You can bootstrap a Neo4j graph with a minimal ontology, a few entities, and mapped relationships to test queries. Graph database selection involves choosing appropriate storage and querying strategies plus implementing data governance.

How does entity resolution work when extracting relationships across domains?▼

Entity resolution during relationship extraction automates the discovery and validation of entities against a defined ontology. It assigns confidence scores to relationships to ensure graph accuracy and support cross-domain AI grounding.