A professional-grade toolkit for AI-native ontology engineering, enabling automated generation, validation, and lifecycle management of RDF and OWL knowledge graphs.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install open-ontologies
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install open-ontologies using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Open Ontologies?
Open Ontologies is a powerful suite designed for developers and data architects who need to build high-fidelity knowledge graphs. By leveraging a comprehensive set of 39+ MCP tools backed by an in-memory Oxigraph triple store, this skill allows AI agents to perform complex semantic web tasks locally without the need for heavy external databases or JVM environments. It bridges the gap between natural language requirements and formal semantic structures like RDF, OWL, and SPARQL.
This Openclaw Skills integration empowers users to implement sophisticated modeling methodologies, including BORO and 4D modeling, while maintaining strict governance through a Terraform-style lifecycle. Whether you are mapping clinical terminologies or automating data ingestion into a graph format, Open Ontologies provides the precision and rigor required for production-grade semantic engineering.
Open Ontologies Use Cases
- Designing and validating complex OWL/RDF ontologies from natural language competency questions.
- Migrating legacy structured data like CSV, JSON, or Parquet into semantic knowledge graphs.
- Implementing clinical terminology crosswalks between ICD-10, SNOMED CT, and MeSH.
- Managing ontology evolution with safety checks, drift detection, and rollback capabilities.
- Performing automated alignment and mapping between disparate schemas or enterprise ontologies.
How Open Ontologies Works
- The process begins with generating Turtle or OWL definitions based on domain requirements or methodology constraints.
- The skill uses onto_validate to ensure syntax correctness before loading the definitions into the local Oxigraph triple store.
- Developers verify the internal structure using SPARQL queries (onto_query) and automated linting (onto_lint) to ensure metadata completeness.
- Refinement occurs through a generate-validate-iterate loop until the ontology meets all statistical and logical expectations.
- The final model is persisted to disk or pushed to a remote SPARQL endpoint for integration into broader systems.
Open Ontologies Setup
To utilize this skill, you must first install the Open Ontologies MCP server. Ensure you have the Rust toolchain installed for the easiest setup.
cargo install open-ontologies
Alternatively, download the binary from the official GitHub releases. Once installed, add the server to your Openclaw Skills configuration or Claude settings:
{
"mcpServers": {
"open-ontologies": {
"command": "open-ontologies",
"args": ["serve"]
}
}
}
Open Ontologies Data Schema & Taxonomy
Open Ontologies manages data through a local, in-memory triple store. It supports various serialization formats and maintains a session-based lineage for governance.
| Data Type | Description |
|---|---|
| RDF Formats | Supports Turtle, N-Triples, RDF/XML, N-Quads, and TriG. |
| Snapshots | Named version snapshots are stored for rollback and history tracking. |
| Rule Packs | Standardized enforcement sets including generic, boro, and value_partition. |
| Mappings | JSON-based configuration files linking structured data schemas to ontology IRIs. |
Open Ontologies Advanced Features
- Terraform-style Lifecycle Management: Use onto_plan and onto_apply to manage schema changes with risk scoring and blast radius analysis.
- SHACL Validation: Enforce data integrity and cardinality constraints directly against your loaded knowledge graph.
- Clinical Crosswalks: Specialized tools for mapping and enriching healthcare data with standardized medical codes.
- Self-Calibrating Alignment: An onto_align feedback loop that improves candidate detection confidence based on user input.
- Automated Reasoning: Built-in support for RDFS and OWL-RL inference to materialize hidden relationships in your data.
SKILL.md
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