DAME: Domain Adaptation for Matching Entities

This repository contains source code for the DAME model, a new domain adaptation-based method that transfers the task knowledge from multiple source domains to a target domain for entity matching (EM). DAME presents a new setting for EM where the objective is to capture the task-specific knowledge from pretraining our model using multiple source domains, then testing our model on a target domain. DAME presents a solution for the zero-shot learning case on the target domain in EM.

Installation

First, install the conda environment dame with supporting libraries.

conda create --name dame python=3.7
conda activate dame
pip install -r requirements.txt

Data

We use 12 datasets in our experiments. Datasets are collected from the entity resolution Benchmark datasets and the Magellan data repository. These datasets cover multiple domains including clothing, electronics, citation, restaurant, products, music, and software. Each dataset is composed of candidate pairs of records from two structured tables that have the same set of attributes. We obtained the datasets from the Ditto repo.

Training with DAME

To train the matching model with DAME:

python train.py \
 --dataset_loc entity-matching-dataset
 --n_gpu 1
 --n_epochs 3
 --model_dir moe_dame
 --batch_size 16
 --lr 1e-5

where the flags are:

• --dataset_loc: the data location
• --n_gpu: set to 1 for using GPU
• --n_epochs: number of epochs
• --model_dir: the output folder where to store models
• --batch_size: batch size for training and testing
• --lr: learning rate for training

Zero-shot results on the target domain

To test DAME on the target domain:

python zero_shot_learning.py \
 --dataset_loc entity-matching-dataset
 --n_gpu 1
 --pretrained_multi_xformer moe_dame
 --batch_size 16

where the flags are:

• --dataset_loc: the data location
• --n_gpu: set to 1 for using GPU
• --pretrained_multi_xformer: the location of trained models
• --batch_size: batch size for testing

Fine-tuning of DAME on the target domain with different percentages of training data

To fine-tune DAME on the target domain:

python fine_tuning_target_domain.py \
 --dataset_loc entity-matching-dataset
 --n_gpu 1
 --pretrained_multi_xformer moe_dame
 --batch_size 16

Fine-tuning of DAME on the target domain with Active Learning (AL)

To fine-tune DAME on the target domain using AL:

python test_target_active_learning.py \
 --dataset_loc entity-matching-dataset
 --n_gpu 1
 --model_dir moe_dame
 --batch_size 16
 --active_learning usde

where the flag `--active_learning`` denotes the method used for Active Learning, and should be one of these options: ['random sampling','least confidence', 'entropy sampling', 'usde', 'bald', 'k-centers', 'k-means', 'core-set']

Dropping expert models from source domains

Reference

If you plan to use DAME in your project, please consider citing our paper:

@inproceedings{trabelsi22wsdm,
author = {Trabelsi, Mohamed and Heflin, Jeff and Cao, Jin},
title = {DAME: Domain Adaptation for Matching Entities},
year = {2022},
isbn = {9781450391320},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3488560.3498486},
doi = {10.1145/3488560.3498486},
booktitle = {Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining},
pages = {1016–1024},
numpages = {9},
keywords = {entity matching, domain adaptation, transfer learning},
location = {Virtual Event, AZ, USA},
series = {WSDM '22}
}

Contact

if you have any questions, please contact Mohamed Trabelsi at mot218@lehigh.edu