Code for paper "A Critical Assessment of State-of-the-Art in Entity Alignment" (https://arxiv.org/abs/2010.16314)

Last update: Oct 14, 2022

Overview

A Critical Assessment of State-of-the-Art in Entity Alignment

This repository contains the source code for the paper

A Critical Assessment of State-of-the-Art in Entity Alignment
Max Berrendorf, Ludwig Wacker, and Evgeniy Faerman
https://arxiv.org/abs/2010.16314

Installation

Setup and activate virtual environment:

python3.8 -m venv ./venv
source ./venv/bin/activate

Install requirements (in this virtual environment):

pip install -U pip
pip install -U -r requirements.txt

In order to run the DGMC scripts, you additionally need to setup its requirements as described in the corresponding GitHub repository's README. We do not include them into requirements.txt, since their installation is a bit more involved, including non-Python dependencies.

Preparation

MLFlow

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

mlflow server

Note: When storing the result for many configurations, we recommend to setup a database backend following the instructions. For the following examples, we assume that the server is running at

TRACKING_URI=http://localhost:5000

OpenEA RDGCN embeddings

Please download the RDGCN embeddings extracted with the OpenEA codebase from here and place them in ~/.kgm/openea_rdgcn_embeddings. They require around 160MiB storage.

BERT initialization

To generate data for the BERT-based initialization, run

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

We also provide preprocessed files at this url. If you prefer to use those, please download and place them in ~/.kgm/bert_prepared. They require around 6.1GiB storage.

Experiments

For all experiments the results are logged to the running MLFlow instance.

Note: The hyperparameter searches takes a significant amount of time (~multiple days), and requires access to GPU(s). You can abort the script at any time, and inspect the current results via the web interface of MLFlow.

Zero-Shot

For the zero-shot evaluation run

(venv) PYTHONPATH=./src python3 executables/zero_shot.py --tracking_uri=${TRACKING_URI}

GCN-Align

To run the hyperparameter search run

(venv) PYTHONPATH=./src python3 executables/tune_gcn_align.py --tracking_uri=${TRACKING_URI}

RDGCN

To run the hyperparameter search run

(venv) PYTHONPATH=./src python3 executables/tune_rdgcn.py --tracking_uri=${TRACKING_URI}

DGMC

To run the hyperparameter search run

(venv) PYTHONPATH=./src python3 executables/tune_dgmc.py  --tracking_uri=${TRACKING_URI}

Evaluation

To summarize the dataset statistics run

(venv) PYTHONPATH=./src python3 executables/summarize.py --target datasets --force

To summarize all experiments run

(venv) PYTHONPATH=./src python3 executables/summarize.py --target results --tracking_uri=${TRACKING_URI} --force

To generate the ablation study table run

(venv) PYTHONPATH=./src python3 executables/summarize.py --target ablation --tracking_uri=${TRACKING_URI} --force

You might also like...

Pytorch implementation of Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization https://arxiv.org/abs/2008.11646

[TCSVT] Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization LPN [Paper] NEWs Prerequisites Python 3.6 GPU Memory = 8G Numpy 1.

46 Dec 14, 2022

https://arxiv.org/abs/2102.11005

LogME LogME: Practical Assessment of Pre-trained Models for Transfer Learning How to use Just feed the features f and labels y to the function, and yo

149 Dec 19, 2022

Official Implementation for "ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement" https://arxiv.org/abs/2104.02699

ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement Recently, the power of unconditional image synthesis has significantly advanced th

967 Jan 4, 2023

ISTR: End-to-End Instance Segmentation with Transformers (https://arxiv.org/abs/2105.00637)

This is the project page for the paper: ISTR: End-to-End Instance Segmentation via Transformers, Jie Hu, Liujuan Cao, Yao Lu, ShengChuan Zhang, Yan Wa

182 Dec 19, 2022

Non-Official Pytorch implementation of "Face Identity Disentanglement via Latent Space Mapping" https://arxiv.org/abs/2005.07728 Using StyleGAN2 instead of StyleGAN

Face Identity Disentanglement via Latent Space Mapping - Implement in pytorch with StyleGAN 2 Description Pytorch implementation of the paper Face Ide

58 Dec 24, 2022

Minimal implementation of PAWS (https://arxiv.org/abs/2104.13963) in TensorFlow.

Unofficial implementation of "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" (https://arxiv.org/abs/2103.14030)

Swin-Transformer-Tensorflow A direct translation of the official PyTorch implementation of "Swin Transformer: Hierarchical Vision Transformer using Sh

52 Dec 29, 2022

Code for paper "A Critical Assessment of State-of-the-Art in Entity Alignment" (https://arxiv.org/abs/2010.16314)

Related tags

Overview

A Critical Assessment of State-of-the-Art in Entity Alignment

Installation

Preparation

MLFlow

OpenEA RDGCN embeddings

BERT initialization

Experiments

Zero-Shot

GCN-Align

RDGCN

DGMC

Evaluation

You might also like...

Pytorch implementation of Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization https://arxiv.org/abs/2008.11646

https://arxiv.org/abs/2102.11005

Official Implementation for "ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement" https://arxiv.org/abs/2104.02699

ISTR: End-to-End Instance Segmentation with Transformers (https://arxiv.org/abs/2105.00637)

Non-Official Pytorch implementation of "Face Identity Disentanglement via Latent Space Mapping" https://arxiv.org/abs/2005.07728 Using StyleGAN2 instead of StyleGAN

Minimal implementation of PAWS (https://arxiv.org/abs/2104.13963) in TensorFlow.

YOLO5Face: Why Reinventing a Face Detector (https://arxiv.org/abs/2105.12931)

A PyTorch implementation of EventProp [https://arxiv.org/abs/2009.08378], a method to train Spiking Neural Networks

Unofficial implementation of "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" (https://arxiv.org/abs/2103.14030)

Releases(v1.1.1)

v1.1.1(Mar 8, 2021)

1.1.0(Dec 31, 2020)

1.0.0(Oct 30, 2020)

Owner

Max Berrendorf

Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition

Machine Learning Toolkit for Kubernetes

PyTorch-LIT is the Lite Inference Toolkit (LIT) for PyTorch which focuses on easy and fast inference of large models on end-devices.

A Player for Kanye West's Stem Player. Sort of an emulator.

A PyTorch Implementation of "SINE: Scalable Incomplete Network Embedding" (ICDM 2018).

Image morphing without reference points by applying warp maps and optimizing over them.

MTCNN face detection implementation for TensorFlow, as a PIP package.

Scaling and Benchmarking Self-Supervised Visual Representation Learning

As a part of the HAKE project, includes the reproduced SOTA models and the corresponding HAKE-enhanced versions (CVPR2020).

Implementation of the paper ''Implicit Feature Refinement for Instance Segmentation''.

Tensorforce: a TensorFlow library for applied reinforcement learning

EfficientMPC - Efficient Model Predictive Control Implementation

Implementation of paper "Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal"

Readings for "A Unified View of Relational Deep Learning for Polypharmacy Side Effect, Combination Therapy, and Drug-Drug Interaction Prediction."

This is an example of a reproducible modelling project

Implementation of CVPR'2022:Reconstructing Surfaces for Sparse Point Clouds with On-Surface Priors

PyTorch implementation for Stochastic Fine-grained Labeling of Multi-state Sign Glosses for Continuous Sign Language Recognition.

Weakly-supervised object detection.

“英特尔创新大师杯”深度学习挑战赛 赛道3：CCKS2021中文NLP地址相关性任务

Learning to Prompt for Vision-Language Models.

“英特尔创新大师杯”深度学习挑战赛赛道3：CCKS2021中文NLP地址相关性任务