OntoCheck
Query-Driven Ontology Assessment for Scientific Domain Applications
OntoCheck is an open-source Python tool that unifies domain-agnostic structural metrics with a novel, query-driven assessment methodology. By analyzing SPARQL queries derived from natural-language competency questions, OntoCheck compares the required query terms against an ontology’s full vocabulary to yield complementary metrics for vocabulary coverage and utilization density.
This empowers domain scientists and data engineers to make evidence-based decisions about ontology selection without requiring deep expertise in formal knowledge representation.
Key Capabilities
17 task-agnostic metrics spanning labeling quality, structural integrity, accessibility compliance, and naming conventions.
Task-based assessment that measures vocabulary coverage (Relevance) and utilization density (Accuracy) against SPARQL competency questions.
Command-line and Python interfaces for integration into automated workflows.
Extensible architecture that supports user-defined metrics and domain-specific question sets.
Quick Install
pip install OntoCheck
Getting Started
# Run all task-agnostic metrics on an ontology
ontocheck path/to/ontology.ttl --metrics all
from ontocheck import run_ontology_assessment, task_based_metric_v_0_0_1
# Task-agnostic assessment
run_ontology_assessment("ontology.ttl", metrics="all")
# Task-based assessment against competency questions
result = task_based_metric_v_0_0_1(
ttl_file="ontology.ttl",
questions="competency_questions.json",
domain_prefixes=["mds"],
)
User Guide
API Reference
Additional Information