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KROMA

Ontology matching with knowledge retrieval and large language models

PyPI Docs ISWC 2025 License: BSD 3-Clause

Documentation · Quick start · Example · Results · Paper · Citation

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. See the documentation for all options.

Example

Try KROMA with a local Ollama model, no API key needed:

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
Source Target Confidence (0-10)
heart cardiac organ 10
lung lungs 8
organ body organ 8

Run with llama3.2:1b on a small demo dataset; see the example for details.

Results

F1 (%) on six OAEI tracks, from the ISWC 2025 paper:

Track KROMA Best baseline
Mouse–Human 94.94 92.20
NCIT–DOID 98.63 94.80
Nell–DBpedia 97.08 96.00
YAGO–Wikidata 95.54 92.19
ENVO–SWEET 93.18 83.70
MI–MatOnto 61.25 32.97

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

Reproducing 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.

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