Query-Driven Ontology Assessment for Scientific Domain Applications
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.
OntoCheck is available as a PyPI package: https://pypi.org/project/OntoCheck/
pip install OntoCheckRequirements: Python 3.8 or later.
OntoCheck takes a declarative configuration C = (O, Q, M), where:
- O — Ontology: one or more ontology files under evaluation (
.ttl). Multiple files are merged automatically for cross-domain assessment. - Q — Questions: competency questions encoded as SPARQL queries (
.jsonor.md). When provided, task-based Relevance and Accuracy are computed automatically. - M — Metrics: the task-agnostic evaluation metrics to compute (structural, labeling, accessibility, naming).
Users select which metrics to run and, optionally, provide competency questions — there is no need to choose a "mode."
# Display available options
ontocheck -h
# Run specific task-agnostic metrics
ontocheck path/to/ontology.ttl --metrics altLabelCheck definitionCheck
# Run all task-agnostic metrics
ontocheck path/to/ontology.ttl --metrics all
# Task-based assessment (Relevance / Accuracy)
ontocheck path/to/ontology.ttl \
--questions competency_questions.json \
--domain-prefixes mds
# Combined: task-based + all agnostic metrics
ontocheck path/to/ontology.ttl \
--metrics all \
--questions competency_questions.json \
--domain-prefixes mds
# Cross-domain: merge multiple ontologies
ontocheck xrd.ttl capacitors.ttl \
--questions cross_domain_questions.json \
--domain-prefixes mds
# Custom output paths
ontocheck path/to/ontology.ttl --metrics all --log-file results.log --csv-file results.csvfrom ontocheck import run_assessment
# Run specific task-agnostic metrics
run_assessment(
ttl_files="path/to/ontology.ttl",
metrics=["altLabelCheck", "definitionCheck", "isolatedElements"],
)
# Task-based assessment (Relevance / Accuracy)
result = run_assessment(
ttl_files="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%}")
# Combined: task-based + all agnostic metrics
result = run_assessment(
ttl_files="path/to/ontology.ttl",
metrics="all",
questions="competency_questions.json",
domain_prefixes=["mds"],
)
# Cross-domain: merge multiple ontologies
result = run_assessment(
ttl_files=["xrd.ttl", "capacitors.ttl"],
questions="cross_domain_questions.json",
domain_prefixes=["mds"],
)OntoCheck provides 17 task-agnostic metrics organized into four categories, along with a task-based assessment methodology.
| 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 |
| 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) |
| 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 |
| 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 |
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.
OntoCheck is conceived as a community resource: we actively encourage collaboration, contribution of new metrics, and submission of domain competency question sets, in the shared interest of building robust, reusable semantic infrastructure for FAIR scientific data.
Full documentation is available at ontocheck.readthedocs.io.
- 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
* These authors contributed equally to this project.
Materials Data Science for Stockpile Stewardship Center of Excellence (MDS3 COE), Case Western Reserve University, Cleveland, OH 44106, USA
We are grateful to the MDS-Onto user community, who are also early users of OntoCheck, across several universities and organizations whose feedback and real-world use cases have directly shaped the tool's development. This material is based upon research in the Materials Data Science for Stockpile Stewardship Center of Excellence (MDS3 COE), and supported by the Department of Energy's National Nuclear Security Administration under Award Number DE-NA0004104. All authors thank the CWRU University Technology Center and the UCF Advanced Research Computing Center for their High Performance Computing (HPC) resources, which were utilized in this work.
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/
OntoCheck is released under the Creative Commons Attribution 4.0 International License.