https://github.com/mberr/rank-based-evaluation

Code for the paper "On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods" (https://arxiv.org/abs/2002.06914)
https://github.com/mberr/rank-based-evaluation

entity-alignment evaluation-metrics knowledge-graph

Last synced: about 1 month ago
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Code for the paper "On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods" (https://arxiv.org/abs/2002.06914)

• Host: GitHub
• URL: https://github.com/mberr/rank-based-evaluation
• Owner: mberr
• License: mit
• Created: 2020-10-30T18:30:18.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2021-03-08T14:34:43.000Z (about 4 years ago)
• Last Synced: 2025-03-24T12:56:18.629Z (about 2 months ago)
• Topics: entity-alignment, evaluation-metrics, knowledge-graph
• Language: Python
• Homepage:
• Size: 71.3 KB
• Stars: 8
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods

[![Python 3.8](https://img.shields.io/badge/Python-3.8-2d618c?logo=python)](https://docs.python.org/3.8/)

[![PyTorch](https://img.shields.io/badge/Made%20with-PyTorch-ee4c2c?logo=pytorch)](https://pytorch.org/docs/stable/index.html)

[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)

This repository contains the code for the paper

```

On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods

Max Berrendorf, Evgeniy Faerman, Laurent Vermue and Volker Tresp

```

# Installation

Setup and activate virtual environment:

```shell script

python3.8 -m venv ./venv

source ./venv/bin/activate

```

Install requirements (in this virtual environment):

```shell script

pip install -U pip

pip install -U -r requirements.txt

```

## MLFlow

In order to track results to a MLFlow server, start it first by running

```shell script

mlflow server

```

# GCN experiments on DBP15k

To run the experiments on DBP15k use

```shell script

(venv) PYTHONPATH=./src python3 executables/adjusted_ranking_experiments.py

```

The results are logged to the running MLFlow instance.

Once finished, you can summarize the results and reproduce the visualization by

```shell script

(venv) python3 executables/summarize.py

```

# Degree investigations

To rerun the experiments for investigating the correlation between node degree, matchings 

and entity representation norms, run

```shell script

(venv) PYTHONPATH=./src python3 executables/degree_investigation.py

```