A package for assessing the quality and structure of ontologies.
Project description
OntoCheck
Query-Driven Ontology Assessment for Scientific Domain Applications
Overview
As scientific fields increasingly adopt FAIR data principles, ontologies have become essential for encoding the semantics of scientific investigations. Yet evaluating ontology quality remains a manual, technically demanding bottleneck. Current frameworks emphasize structural correctness but fail to assess practical utility against the real-world queries posed by domain scientists.
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.
OntoCheck is actively developed and maintained by the SDLE Research Center at Case Western Reserve University.
Installation
pip install OntoCheck
Requirements: Python 3.8 or later.
Quick Start
Command-Line Interface
# Display available metrics and usage information
ontocheck -h
# Run specific metrics on an ontology file
ontocheck path/to/ontology.ttl --metrics altLabelCheck definitionCheck
# Run all available task-agnostic metrics
ontocheck path/to/ontology.ttl --metrics all
# Specify custom output file paths
ontocheck path/to/ontology.ttl --metrics all --log-file results.log --csv-file results.csv
Python API
from ontocheck import run_ontology_assessment
# Run selected metrics
run_ontology_assessment(
ttl_file="path/to/ontology.ttl",
metrics=["altLabelCheck", "definitionCheck", "isolatedElements"],
)
# Run all task-agnostic metrics
run_ontology_assessment(
ttl_file="path/to/ontology.ttl",
metrics="all",
)
Task-Based Assessment
from ontocheck import task_based_metric
result = task_based_metric(
ttl_file="path/to/ontology.ttl",
questions="competency_questions.json",
domain_prefixes=["mds"],
domain_ns_fragments=["cwrusdle.bitbucket.io/mds"],
)
print(f"Relevance: {result['relevance']:.2%}")
print(f"Accuracy: {result['accuracy']:.2%}")
Available Metrics
OntoCheck provides 17 task-agnostic metrics organized into four categories, along with a task-based assessment methodology.
Labeling
| Metric | Function | Description |
|---|---|---|
checkLabel |
mainLabelCheck_v_0_0_1 |
Proportion of named classes carrying human-readable identifiers |
altLabelCheck |
mainAltLabelCheck_v_0_0_1 |
Proportion of named classes carrying synonyms |
definitionCheck |
mainDefCheck_v_0_0_1 |
Proportion of named classes carrying formal definitions |
Structural
| Metric | Function | Description |
|---|---|---|
isolatedElements |
check_for_isolated_elements |
Identifies orphaned classes within the ontology |
classConnections |
count_class_connected_components |
Identifies disconnected subgraphs |
missingDomainRange |
get_properties_missing_domain_and_range |
Identifies undeclared domain and range restrictions |
leafNodeCheck |
mainLeafNodeCheck_v_0_0_1 |
Identifies all leaf nodes in the ontology hierarchy |
semanticConnection |
mainSemanticConnection_v_0_0_1 |
Verifies grounding in upper-level ontologies (e.g., CCO, BFO) |
Accessibility
| Metric | Function | Description |
|---|---|---|
sparqlEndpoint |
check_sparql_accessibility_ttl |
Verifies reachability of the SPARQL endpoint |
rdfDump |
check_rdf_dump_accessibility_ttl |
Verifies availability of the RDF data dump |
humanLicense |
check_human_readable_license_ttl |
Verifies presence and fitness of licensing information |
externalLinks |
check_external_data_provider_links_ttl |
Checks validity of external links within the ontology |
Naming Convention
| Metric | Function | Description |
|---|---|---|
classCapitalCheck |
mainClassNameCapitalCheck_v_0_0_1 |
Flags departures from standard capitalization |
classSpaceCheck |
mainClassNameSpaceCheck_v_0_0_1 |
Flags use of spaces in class identifiers |
spellCheck |
spell_check_v_0_0_1 |
Spell checking on labels and definitions |
duplicateLabels |
find_duplicate_labels_from_graph |
Identifies duplicate labels across entities |
searchClass |
mainClassSearch_v_0_0_1 |
Identifies classes matching a user-specified string |
Task-Based Assessment
The task-based methodology measures how well an ontology supports analytical queries by computing two complementary metrics from SPARQL competency questions:
- Relevance = |T_a intersection T_o| / |T_a| -- the fraction of task-required terms that the ontology defines
- Accuracy = |T_a intersection T_o| / |T_o| -- the fraction of ontology terms utilized by the task queries
where T_a is the set of domain terms extracted from the SPARQL queries and T_o is the set of domain terms defined in the ontology.
Documentation
Full documentation is available at ontocheck.readthedocs.io.
Authors
- Rishabh Kundu
- Redad Mehdi
- Van D. Tran
- Ethan Frakes
- Abhishek Daundkar
- Maliesha Sumudumalie
- Vibha S. Mandayam
- Jacob A. Lample
- Mengjie Li
- Laura S. Bruckman
- Erika I. Barcelos
- Alp Sehirlioglu
- Roger H. French
- Yinghui Wu
Affiliation
Materials Data Science for Stockpile Stewardship Center of Excellence (MDS3 COE), Case Western Reserve University, Cleveland, OH 44106, USA
Acknowledgments
- U.S. Department of Energy's National Nuclear Security Administration -- Award Number DE-NA0004104 and Contract Number B647887
- U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) under the Solar Energy Technologies Office (SETO) -- Agreement Numbers DE-EE0009353 and DE-EE0009347
- U.S. National Science Foundation -- Award Number 2133576
How to Cite
If you use OntoCheck in your work, please cite:
Rishabh Kundu, Redad Mehdi, Van D. Tran, Ethan Frakes, Abhishek Daundkar, Maliesha Sumudumalie, Vibha S. Mandayam, Jacob A. Lample, Mengjie Li, Laura S. Bruckman, Erika I. Barcelos, Alp Sehirlioglu, Roger H. French, Yinghui Wu (2025). OntoCheck: Query-Driven Ontology Assessments for Scientific Domain Applications. [Python]. https://pypi.org/project/OntoCheck/
License
OntoCheck is released under the BSD-2-Clause License.
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