AgreementMakerDeep (AMD) is a new flexible and extensible ontology matching system with PLM and KGE techniques. In OAEI 2022, we only apply zero-shot learning in Bio-ML track. AMD achieved competetive performance in terms of the evaluateion metrics used in the track without extra training. We also upload our alignments results of all dataset in Bio-ML track for reference.

1. Python >= 3.7:

We recommend you to use Anaconda to create a conda environment: conda create -n AMD-Seals python=3.7

conda activate AMD-Seals

2. Other requirements:

   pip install -r requirements.txt

Note: This file is able to produce alignments without MELT package.

1. To evaluate it with previous seals client and MELT Track repository:

   java -jar seals-omt-client.jar AMD-seals -x http:https://oaei.webdatacommons.org/tdrs/ Suite-ID Version-ID /Users/AMD -a

   examples: java -jar /Users/Ellen/Downloads/seals-omt-client.jar /Users/Ellen/Desktop/AMDSeals/target/AMD-seals -x http:https://oaei.webdatacommons.org/tdrs/ largebio largebio-snomed_nci_small_2016 /Users/Ellen/Downloads/AMD -a

2. Re-usage

   If any changes to the model, then use MELT to wrapped and evalute.

For the whole AMD-seals, please refer to AMD for OAEI 2021.

If you find this repo useful and use our code for your work, please consider citing and star this repo:

@inproceedings{wang2021agreementmakerdeep,
  title={AgreementMakerDeep results for OAEI 2021.},
  author={Wang, Zhu and Cruz, Isabel F},
  booktitle={OM@ ISWC},
  pages={124--130},
  year={2021}
}

and

@inproceedings{wang2022amd,
  title={AMD results for OAEI 2022.},
  author={Wang, Zhu},
  booktitle={OM@ ISWC},
  pages={145--152},
  year={2022}
}