Domain Net

Repository Overview

• DATA: a folder that will contain all the input data (Instruction on how to download the data can be found at: https://github.com/northeastern-datalab/DomainNet-Datasets)
• graph_construction: module to construct a graph representation given a repository of tables
• homograph_injection: module to artificially inject homographs in a repository
• network_analysis: module to run network centrality measures on our graph representation and provide a score for each value in the repository.

Setup

1. Clone the repo
2. CD to the repo directory. Create and activate a virtual environment for this project

• On macOS or Linux:

python3 -m venv env
source env/bin/activate
which python

3. Install necessary packages

   pip install -r requirements.txt
   

We recommend using python version 3.8.

Reproducibility

Synthetic Benchmark (SB)

To reproduce our results and analysis on the synthetic benchmark run the synthetic_benchmark.sh script. You can do that by running:

chmod +x synthetic_benchmark.sh && ./synthetic_benchmark.sh

The script will produce the bipartite graph representation for the synthetic benchmark and then calculate the BC scores for every node in that graph. Finally open and run all cells in the synthetic_benchmark_analysis.ipynb jupyter notebook file to see the analysis and produced figures.

Table Union Search (TUS) Benchmark

To reproduce our results and analysis on the table union search (TUS) benchmark run the TUS_benchmark.sh script. You can do that by running:

chmod +x TUS_benchmark.sh && ./TUS_benchmark.sh

The script will produce the bipartite graph representation for the synthetic benchmark and then calculate the approximate BC scores for every node in that graph. 5000 nodes are used for sampling. Finally the precision/recall/f1-score curves at various top-k values will be produced and can be found in the network_analysis/figures/TUS/ directory.