KROMA finds the concepts that two ontologies share. It retrieves likely matches for each concept, accepts identical names directly, and asks a large language model about the rest, showing it each concept's labels, synonyms, parents and children.

Quick start

pip install kroma-om openai
export OPENAI_API_KEY=sk-...

kroma match mouse.owl human.owl -o alignments.csv

Or from Python:

import kroma

result = kroma.match("mouse.owl", "human.owl", model="gpt-4o-mini")
result.save("alignments.csv")    # .csv, .tsv, or .rdf (OAEI format)

KROMA reads any RDF format (.owl, .ttl, .rdf, …) and works with OpenAI, Together AI, or any function that takes a prompt and returns text.

Run it locally

No API key needed, only Ollama and a small model:

git clone https://github.com/cwru-sdle/kroma.git && cd kroma/examples
pip install kroma-om
ollama pull llama3.2:1b
python match_ollama.py --model llama3.2:1b

See the example for details.

Results

F1 (%) on six OAEI tracks, from the paper:

Ablations, other models and cost are in the full paper.

Reproduce the paper

git clone https://github.com/cwru-sdle/kroma.git && cd kroma
./setup.sh && source .venv/bin/activate
uv pip install ".[models]"

kroma run --method_config kroma_scibert_envo_sweet \
          --llm meta-llama/Llama-3.3-70B-Instruct-Turbo-Free

Datasets, configs and flags are described in Benchmark experiments.

Citation

@inproceedings{nguyen_2025_kroma,
  title     = {KROMA: Ontology Matching with Knowledge Retrieval and Large Language Models},
  author    = {Lam Nguyen and Erika Barcelos and Roger French and Yinghui Wu},
  booktitle = {The Semantic Web -- ISWC 2025: 24th International Semantic Web Conference, Proceedings, Part I},
  series    = {Lecture Notes in Computer Science},
  publisher = {Springer},
  pages     = {629--649},
  year      = {2025},
  doi       = {10.1007/978-3-032-09527-5_34}
}

License

BSD 3-Clause · Developed at the SDLE Research Center, Case Western Reserve University.