ingest

Name: ingest
Availability: InStock
Author: Semantica

Ingest data from files, databases, APIs, or streams into Semantica knowledge graphs.

3.0k|350|Updated Jun 25, 2025

Search Tags:#data #etl #knowledge-graph #ingest #data-integration #entity-linking #schema-mapping

Ready to run

npx skills add https://github.com/semantica-agi/semantica --skill ingest-semantica-agi

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ingest
Source: https://github.com/semantica-agi/semantica/tree/main/plugins/skills/ingest
Command: npx skills add https://github.com/semantica-agi/semantica --skill ingest-semantica-agi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Ingest data into Semantica knowledge graphs from multiple sources (files, databases, APIs, or streams), enabling consistent population of structured graphs and enabling downstream reasoning.

Core Features & Use Cases

Ingest structured and semi-structured data from local files, databases, REST APIs, or streaming sources.
Apply schema mapping and entity linking to align ingested data with the knowledge graph ontology.
Use cases include data onboarding, incremental updates, and graph enrichment for decision intelligence.

Quick Start

Ingest a source (e.g., a CSV file or database) using /semantica:ingest <source> to populate the graph with mapped entities.

Frequently Asked Questions about ingest

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

FAQPage Schema

How do I ingest data from a CSV file into a knowledge graph?▼

To ingest data from a CSV file into a knowledge graph, you provide the source to the ingestion workflow, which applies schema mapping and entity linking to populate the graph with structured entities.

Can I ingest data from REST APIs and streaming sources into a knowledge graph?▼

Yes, data ingestion supports REST APIs and streaming sources alongside local files and databases, enabling continuous knowledge graph enrichment and incremental updates for downstream reasoning.

What is schema mapping and entity linking in ETL data integration?▼

Schema mapping and entity linking in data integration align ingested records from multiple sources with the target knowledge graph ontology, ensuring consistent structured graph population and accurate entity relationships.

Do I need to write scripts for knowledge graph population and entity linking?▼

Scripts are optional for knowledge graph population. The data integration workflow handles schema mapping and entity linking automatically, but scripts can support deterministic tasks when needed.

What's the best way to track provenance during knowledge graph data ingestion?▼

The best way to track provenance during knowledge graph data ingestion is to route files, databases, or API streams through the ETL pipeline, which natively records provenance tracking alongside schema mapping.

Does data ingestion from databases work for incremental knowledge graph updates?▼

Yes, data ingestion from databases supports incremental knowledge graph updates, applying schema mapping and entity linking to align new records with the existing ontology for consistent graph enrichment.