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semantica

Create, analyze, and explain knowledge graphs and decision flows for AI systems.

2.9k|348|Updated Jun 25, 2025
Search Tags:#ontology#provenance#graph analysis#explainability#decision intelligence#knowledge graphs
One-click install
npx skills add https://github.com/Hawksight-AI/semantica --skill semantica
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: semantica
Source: https://github.com/Hawksight-AI/semantica/tree/main/.claude/skills/semantica
Command: npx skills add https://github.com/Hawksight-AI/semantica --skill semantica

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides tools for creating and managing knowledge graphs, decision intelligence, and provenance tracking, enhancing AI transparency and trustworthiness.

Core Features & Use Cases

  • Knowledge Graph Construction: Guidance on semantic extraction, entity and relation detection, and triplet generation.
  • Decision and Reasoning Workflows: Support for causal analysis, decision impact, and rule-based inference.
  • Explainability and Provenance: Methods for tracing data sources, explaining reasoning, and ensuring data lineage in AI outputs.
  • Ontology and Policy Enforcement: Assistance with schema validation, concept modeling, and compliance evaluation.
  • Graph Analytics and Export: Tools for analyzing graph topology, identifying influential nodes, and exporting to multiple formats like JSON, RDF, or GraphML.
  • Data Ingestion and Deduplication: Support for integrating data from various sources and cleaning duplicate entities.

Quick Start

Ask me how to extract entities from text and generate a knowledge graph.

Frequently Asked Questions about semantica

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

FAQPage Schema
How do I build a knowledge graph from unstructured text?▼

To build a knowledge graph from text, you perform semantic extraction to detect entities and relations, then generate triplets. This structures raw text into connected data for graph analysis and AI explainability.

What is data provenance and how does it improve AI explainability?▼

Data provenance traces the sources and lineage of information used in AI outputs. Tracking provenance improves AI explainability by providing an auditable trail of reasoning, ensuring transparency in decision intelligence.

How can I export a knowledge graph to RDF or GraphML formats?▼

You export a knowledge graph to JSON, RDF, or GraphML using graph analytics workflows. This allows you to analyze topology, identify influential nodes, and integrate exported data into external systems.

Does ontology modeling support schema validation for compliance enforcement?▼

Yes, ontology modeling supports schema validation and concept modeling to enforce policies. This ensures your knowledge graph complies with predefined structures and evaluates compliance during data integration.

What is the best way to deduplicate entities during data ingestion?▼

The best way to deduplicate entities during data ingestion is using data integration workflows that clean duplicate records. This ensures your knowledge graph maintains accuracy when combining information from various sources.

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