BERTMap is now maintained in the Deeponto repository: https://github.com/KRR-Oxford/DeepOnto
Important Notices
- The relevant paper was accepted in AAAI-2022.
- Arxiv version is available at: https://arxiv.org/abs/2112.02682.
BERTMap is a BERT-based ontology alignment system, which utilizes the textual knowledge of ontologies to fine-tune BERT and make prediction. It also incorporates sub-word inverted indices for candidate selection, and (graph-based) extension and (logic-based) repair modules for mapping refinement.
The following packages are necessary but not sufficient for running BERTMap:
conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch # pytorch
pip install cython # the optimized parser of owlready2 relies on Cython
pip install owlready2 # for managing ontologies
pip install tensorboard # tensorboard logging (optional)
pip install transformers # huggingface library
pip install datasets # huggingface datasets
IMPORTANT NOTICE: BERTMap relies on class labels for training, but different ontologies have different annotation properties to define the aliases (synonyms), so preprocessing is required for adding all the synonyms to rdf:label
before running BERTMap. The preprocessed ontologies involved in our paper together with their reference mappings are available in data.zip
.
Clone the repository and run:
# fine-tuning and evaluate bertmap prediction
python run_bertmap.py -c config.json -m bertmap
# mapping extension (-e specify which mapping set {src, tgt, combined} to be extended)
python extend_bertmap.py -c config.json -e src
# evaluate extended bertmap
python eval_bertmap.py -c config.json -e src
# repair and evluate final outputs (-t specify best validation threshold)
python repair_bertmap.py -c config.json -e src -t 0.999
# baseline models (edit similarity and pretrained bert embeddings)
python run_bertmap.py -c config.json -m nes
python run_bertmap.py -c config.json -m bertembeds
The script skips data construction once built for the first time to ensure that all of the models share the same set of pre-processed data.
The fine-tuning model is implemented with huggingface Trainer, which by default uses multiple GPUs, for restricting to GPUs of specified indices, please run (for example):
# only device (1) and (2) are visible to the script
CUDA_VISIBLE_DEVICES=1,2 python run_bertmap.py -c config.json -m bertmap
Here gives the explanations of the variables used in config.json
for customized BERTMap running.
data
:task_dir
: directory for saving all the output files.src_onto
: source ontology name.tgt_onto
: target ontology name.task_suffix
: any suffix of the task if needed, e.g. the LargeBio track has 'small' and 'whole'.src_onto_file
: source ontology file in.owl
format.tgt_onto_fil
: target ontology file in.owl
format.properties
: list of textual properties used for constructing semantic data , default is class labels:["label"]
.cut
: threshold length for thekeys
of sub-word inverted index, preserve thekeys
only if their lengths >cut
, default is0
.
corpora
:sample_rate
: number of (soft) negative samples for each positive sample generated in corpora (not the ultimate fine-tuning data).src2tgt_mappings_file
: reference mapping file for evaluation and semi-supervised learning setting in.tsv
format with columns:"Entity1"
,"Entity2"
and"Value"
.ignored_mappings_file
: file in.tsv
format but stores mappings that should be ignored by the evaluator.train_map_ratio
: proportion of training mappings to used in semi-supervised setting, default is0.2
.val_map_ratio
: proportion of validation mappings to used in semi-supervised setting, default is0.1
.test_map_ratio
: proportion of test mappings to used in semi-supervised setting, default is0.7
.io_soft_neg_rate
: number of soft negative sample for each positive sample generated in the fine-tuning data at the intra-ontology level.io_hard_neg_rate
: number of hard negative sample for each positive sample generated in the fine-tuning data at the intra-ontology level.co_soft_neg_rate
: number of soft negative sample for each positive sample generated in the fine-tuning data at the cross-ontology level.depth_threshold
: classes of depths larger than this threshold will not considered in hard negative generation, default isnull
.depth_strategy
: strategy to compute the depths of the classes if any threshold is set, default ismax
, choices aremax
andmin
.
bert
pretrained_path
: real or huggingface library path for pretrained BERT, e.g."emilyalsentzer/Bio_ClinicalBERT"
(BioClinicalBERT).tokenizer_path
: real or huggingface library path for BERT tokenizer, e.g."emilyalsentzer/Bio_ClinicalBERT"
(BioClinicalBERT).
fine-tune
include_ids
: include identity synonyms in the positive samples or not.learning
: choice of learning settingss
(semi-supervised) orus
(unsupervised).warm_up_ratio
: portion of warm up steps.max_length
: maximum length for tokenizer (highly important for large task!).num_epochs
: number of training epochs, default is3.0
.batch_size
: batch size for fine-tuning BERT.early_stop
: whether or not to apply early stopping (patience has been set to10
), default isfalse
.resume_checkpoint
: path to previous checkpoint if any, default isnull
.
map
candidate_limits
: list of candidate limits used for mapping computation, suggested values are[25, 50, 100, 150, 200]
.batch_size
: batch size used for mapping computation.nbest
: number of top results to be considered.string_match
: whether or not to use string match before others.strategy
: strategy for classifier scoring method, default ismean
.
eval
:automatic
: whether or not automatically evaluate the mappings.
Should you need any further customizaions especially on the evaluation part, please set eval: automatic
to false
and use your own evaluation script.