open-ontologies

name open-ontologies version 0.5.1 description AI-native ontology engineering using 50+ MCP tools backed by an in-memory Oxigraph triple store. Build, validate, query, and govern RDF/OWL ontologies with a generate-validate-iterate loop. Use when building ontologies, knowledge graphs, RDF data, SPARQL queries, BORO/4D modeling, SHACL validation, clinical terminology mapping, ingesting from CSV/JSON/Parquet/XLSX or SQL backbones (PostgreSQL, DuckDB), or Terraform-style ontology lifecycle management. tags ["ontology","rdf","owl","sparql","knowledge-graph","semantic-web","mcp","oxigraph","shacl","boro"] metadata {"openclaw":{"requires":{"mcp":"[Truncated]","bins":"[Truncated]"},"network":["[Truncated]","[Truncated]"],"notes":"All processing is local by default. The in-memory Oxigraph triple store runs inside the MCP server process -- no database, no JVM, no external services required. Network access is only used by onto_pull and onto_push when the user explicitly provides a remote URL or SPARQL endpoint. onto_monitor alerts are logged locally to stdout; no external notification services are contacted. No credentials or API keys are needed for core functionality.\n"}} Open Ontologies AI-native ontology engineering. Generate OWL/RDF directly, validate with MCP tools, iterate until clean, govern with a Terraform-style lifecycle. Prerequisites This skill requires the Open Ontologies MCP server to provide the onto_* tools. Install: cargo install open-ontologies or download from GitHub releases MCP config (add to .mcp.json or Claude settings): { "mcpServers" : { "open-ontologies" : { "command" : "open-ontologies" , "args" : [ "serve" ] } } } No credentials needed. All processing runs locally in an in-memory Oxigraph triple store. Network access is only used when you explicitly call onto_pull (fetch remote ontology) or onto_push (send to SPARQL endpoint) with a URL you provide. Monitor alerts ( onto_monitor ) are logged to stdout only. Core Workflow When building or modifying ontologies, follow this workflow. Decide which tools to call and in what order based on results -- this is not a fixed pipeline. 1. Generate Understand the domain requirements (natural language, competency questions, methodology constraints) Generate Turtle/OWL directly -- Claude knows OWL, RDF, BORO, 4D modeling natively For complex methodologies, ask for background documents or constraints 2. Validate and Load Call onto_validate on the generated Turtle -- if it fails, fix syntax errors and re-validate Call onto_load to load into the Oxigraph triple store Call onto_stats to verify class count, property count, triple count match expectations 3. Verify Call onto_lint to check for missing labels, comments, domains, ranges -- fix any issues found Call onto_query with SPARQL to verify structure (expected classes, subclass hierarchies, competency questions) If a reference ontology exists, call onto_diff to compare 4. Iterate If any step reveals problems, fix the Turtle and restart from step 2 Continue until validation passes, stats match, lint is clean, and SPARQL queries return expected results 5. Persist Call onto_save to write the final ontology to a .ttl file Call onto_version to save a named snapshot for rollback Ontology Lifecycle (Terraform-style) For evolving ontologies in production: Plan -- onto_plan shows added/removed classes, blast radius, risk score. Check onto_lock for protected IRIs. Enforce -- onto_enforce with a rule pack ( generic , boro , value_partition ) checks design pattern compliance. Apply -- onto_apply with mode safe (clear + reload) or migrate (add owl:equivalentClass bridges). Monitor -- onto_monitor runs SPARQL watchers with threshold alerts. Use onto_monitor_clear if blocked. Drift -- onto_drift compares versions with rename detection and self-calibrating confidence. Data Extension Workflow When applying an ontology to external data: onto_map -- generate mapping config from data schema + loaded ontology onto_ingest -- parse structured data (CSV, JSON, NDJSON, XML, YAML, XLSX, Parquet) into RDF onto_shacl -- validate against SHACL shapes (cardinality, datatypes, classes) onto_reason -- run RDFS or OWL-RL inference, materializing inferred triples Or use onto_extend to run the full pipeline: ingest, SHACL validate, reason in one call Clinical Terminology Support For healthcare ontologies: onto_crosswalk -- look up mappings between ICD-10, SNOMED CT, and MeSH onto_enrich -- add skos:exactMatch triples linking classes to clinical codes onto_validate_clinical -- check class labels against clinical crosswalk terminology Ontology Alignment For aligning two ontologies: onto_align -- detect alignment candidates (equivalentClass, exactMatch, subClassOf) using 6 weighted signals onto_align_feedback -- accept/reject candidates to self-calibrate confidence weights Tool Reference Tool When to use onto_validate After generating or modifying Turtle -- always validate first onto_load After validation passes -- loads into triple store onto_stats After loading -- sanity check on counts onto_lint After loading -- catches missing labels, domains, ranges onto_query Verify structure, answer competency questions onto_diff Compare against a reference or previous version onto_save Persist ontology to a file onto_convert Convert between formats (Turtle, N-Triples, RDF/XML, N-Quads, TriG) onto_clear Reset the store before loading a different ontology onto_pull Fetch ontology from a remote URL or SPARQL endpoint onto_push Push ontology to a SPARQL endpoint onto_import Resolve and load owl:imports chains onto_version Save a named snapshot before making changes onto_history List saved version snapshots onto_rollback Restore a previous version onto_ingest Parse structured data into RDF and load into store onto_map Generate mapping config from data schema + ontology onto_shacl Validate data against SHACL shapes onto_reason Run RDFS or OWL-RL inference onto_extend Full pipeline: ingest, SHACL validate, reason onto_plan Show added/removed classes, blast radius, risk score onto_apply Apply changes in safe or migrate mode onto_lock Protect production IRIs from removal onto_drift Compare versions with rename detection onto_enforce Design pattern checks: generic, boro, value_partition, or custom onto_monitor Run SPARQL watchers with threshold alerts onto_monitor_clear Clear blocked state after resolving alerts onto_crosswalk Look up clinical terminology mappings (ICD-10, SNOMED, MeSH) onto_enrich Add skos:exactMatch triples linking to clinical codes onto_validate_clinical Check class labels against clinical terminology onto_align Detect alignment candidates between two ontologies onto_align_feedback Accept/reject alignment candidates for self-calibrating weights onto_lineage View session lineage trail (plan, enforce, apply, monitor, drift) onto_lint_feedback Accept/dismiss lint issues to teach suppression onto_enforce_feedback Accept/dismiss enforce violations to teach suppression onto_unload Unload from memory. With name targets a specific cached entry; delete_cache=true also removes the on-disk file onto_recompile Re-parse the source. With name rebuilds a non-active cached entry without disturbing the active in-memory store onto_cache_status Inspect compile cache: active slot, all entries, effective [cache] config onto_cache_list List cached ontologies with metadata ( is_active , in_memory , mtime, size) onto_cache_remove Remove a cached ontology by name (pass delete_file=false to keep the on-disk N-Triples) onto_repo_list List RDF/OWL files in configured [general] ontology_dirs directories onto_repo_load Load an ontology from a configured repo by bare name, relative path, or absolute path onto_status Server health / loaded triple count onto_marketplace Browse / install standard ontologies from the curated catalogue onto_dl_check Check subClass ⊑ superClass via DL tableaux onto_dl_explain Explain why a class is unsatisfiable (DL clash trace) onto_embed Generate text + Poincaré structural embeddings for all classes onto_search Natural-language query → most-similar classes onto_similarity Cosine + Poincaré distance between two IRIs onto_import_schema Introspect PostgreSQL or DuckDB schema → generate OWL classes/properties/cardinality onto_sql_ingest Run SQL SELECT against PostgreSQL or DuckDB → RDF (DuckDB enables federation over CSV/Parquet/JSON/HTTPFS/postgres-scanner via its extensions) Usage Examples Build a pizza ontology from scratch Build me a pizza ontology with classes for Pizza, PizzaBase (ThinAndCrispy, DeepPan), PizzaTopping (Mozzarella, Tomato, Pepperoni, Mushroom), and properties hasBase, hasTopping. Include rdfs:labels and rdfs:comments on everything. Validate and run competency queries to check I can ask "what toppings does a Margherita have?" Load and query an existing ontology Load the ontology from https://www.w3.org/TR/owl-guide/wine.rdf, show me stats, lint it, and run a SPARQL query to find all subclasses of Wine. Evolve an ontology safely I need to add a new class "GlutenFreePizza" as a subclass of Pizza with a restriction that hasBase only GlutenFreeBase. Plan the change, enforce against generic rules, and apply in safe mode. Ingest CSV data into a knowledge graph I have a CSV of employees with columns: name, department, role, start_date. Map it to the loaded HR ontology and ingest it. Then validate with SHACL shapes and run inference to materialize department hierarchies. Align two ontologies Load schema.org and my company ontology. Run onto_align to find equivalentClass and exactMatch candidates. I'll review and give feedback to calibrate the weights. Key Principle Dynamically decide the next tool call based on what the previous tool returned. If onto_validate fails, fix and retry. If onto_stats shows wrong counts, regenerate. If onto_lint finds missing labels, add them. The MCP tools are individual operations -- Claude is the orchestrator.