BERTMap: A BERT-Based Ontology Alignment System
Authors: Yuan He, Jiaoyan Chen, Denvar Antonyrajah, Ian Horrocks5684-5691
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our evaluation with three alignment tasks on biomedical ontologies demonstrates that BERTMap can often perform better than the leading OM systems Log Map and AML. |
| Researcher Affiliation | Collaboration | 1 Department of Computer Science, University of Oxford, UK 2 Samsung Research, UK {yuan.he,jiaoyan.chen,ian.horrocks}@cs.ox.ac.uk, denvar.a@samsung.com |
| Pseudocode | Yes | Algorithm 1: Iterative Mapping Extension |
| Open Source Code | Yes | 1Codes and data: https://github.com/KRR-Oxford/BERTMap. |
| Open Datasets | Yes | The evaluation considers the FMA-SNOMED and FMA-NCI small fragment tasks of the OAEI Large Bio Track. They have large-scale ontologies and high quality gold standards created by domain experts. |
| Dataset Splits | Yes | In the unsupervised setting, we divide M= into Mval (10%) and Mtest (90%); and in the semi-supervised setting, we divide M= into Mtrain (20%), Mval (10%) and Mtest (70%). |
| Hardware Specification | Yes | The training uses a single GTX 1080Ti GPU. |
| Software Dependencies | No | The paper mentions that the implementation uses 'owlready2' and 'transformers' libraries but does not provide specific version numbers for them. |
| Experiment Setup | Yes | The BERT model is fine-tuned for 3 epochs with a batch size of 32, and evaluated on the validation set for every 0.1 epoch, through which the best checkpoint on the cross-entropy loss is selected for prediction. The cut-off of sub-word inverted index-based candidate selection is set to 200. Besides, we set the positive-negative sample ratio to 1 : 4. |