SilkMoth/experiments

🧪 Running the Experiments

This project includes multiple experiments to evaluate the performance and accuracy of our Python implementation of SilkMoth.

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📊 1. Experiment Types

You can replicate and customize the following types of experiments using different configurations (e.g., filters, signature strategies, reduction techniques):

• String Matching (DBLP Publication Titles)
• Schema Matching (WebTables)
• Inclusion Dependency Discovery (WebTable Columns)

Exact descriptions can be found in the official paper.

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📦 2. WebSchema Inclusion Dependency Setup

To run the WebSchema + Inclusion Dependency experiments:

1. Download the pre-extracted dataset from
   📥 this link.
2. Place the .json files in the data/webtables/ directory
   (create the folder if it does not exist).

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🚀 3. Running the Experiments

To execute the core experiments from the paper:

python run.py

📈 4. Results Overview

We compared our results with those presented in the original SilkMoth paper.
Although exact reproduction is not possible due to language differences (Python vs C++) and dataset variations, overall performance trends align well.

All the results can be found in the folder results.

The left diagrams are from the paper and the right are ours.

💡 Recent performance enhancements leverage scipy’s C-accelerated matching, replacing the original networkx-based approach.
Unless otherwise specified, the diagrams shown are generated using the networkx implementation.

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🔍 Inclusion Dependency

Goal: Check if each reference set is contained within source sets.

Filter Comparison

Signature Comparison

Reduction Comparison

Scalability

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🔍 Schema Matching (WebTables)

Goal: Detect related set pairs within a single source set.

Filter Comparison

Signature Comparison

Scalability

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🔍 String Matching (DBLP Publication Titles)

Goal: Detect related titles within the dataset using the extended SilkMoth pipeline based on edit similarity and q-gram tokenization. SciPy was used here.

Filter Comparison

Signature Comparison

Scalability

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🔍 Additional: Inclusion Dependency SilkMoth Filter compared with no SilkMoth

In this analysis, we focus exclusively on SilkMoth. But how does it compare to a brute-force approach that skips the SilkMoth pipeline entirely? The graph below shows the Filter run alongside the brute-force bipartite matching method without any optimization pipeline. The results clearly demonstrate a dramatic improvement in runtime efficiency when using SilkMoth.

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🔍 Additional: Schema Matching with GitHub WebTables

Similar to Schema Matching, this experiment uses a GitHub WebTable as a fixed reference set and matches it against other sets. The goal is to evaluate SilkMoth’s performance across different domains. Left: Matching with one reference set. Right: Matching with WebTable Corpus and GitHub WebTable datasets. The results show no significant difference, indicating consistent behavior across varying datasets.