wbsg-uni-mannheim / wdc-lspc-v2 Goto Github PK

This repository contains code and data download scripts for the paper Using schema.org annotations for training and maintaining product matchers by Ralph Peeters, Anna Primpeli, Benedikt Wichtlhuber and Christian Bizer.

1. anaconda (or similar for standard packages)
2. py_entitymatching
3. xgboost
4. deepmatcher

Update: Added an environment yml (wdc-lspc-v2.yml), which can be used to create a conda environment similar to the one used. Simply run conda env create -f wdc-lspc-v2.yml.

Update: Added scripts to download either the normalized or non-normalized versions of the training/validation/gold standard sets. Please only use one of them by navigating to the src/data/ folder and run e.g. python download_datasets_normalized.py to automatically download the files into the correct locations. You can then find the data at data/raw/. This download does not include the corresponding corpus file. If you need this, you have to download it from the project website yourself. Note that the non-normalized data may need some additional pre-processing, the experiments were done using the normalized data.

(If you do not want to use the download scripts: please download and unzip the WDC LSPC v2 normalized data files into the corresponding folder under data/raw/wdc-lspc/)

1. Run noise-training-sets notebook <- creates noised training sets
2. Run process-to-magellan and process-to-wordcooc notebooks <- prepares input data for experiments

Run run-wordcooc, run-magellan or run-deepmatcher notebooks to replicate learning curve and label-noise experiments.

Find the best parameter combinations in the file optimized-parameters.txt

To allow for gradient updates of the embedding layer, simply change the line embed.weight.requires_grad = False in models/core.py to True in the deepmatcher package

Additional requirement: textdistance

The notebook sample-training-sets contains the code used for building the 4 training sets for each product category

Project structure based on Cookiecutter Data Science: https://drivendata.github.io/cookiecutter-data-science/