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

Overview

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

Arxiv Python 3.8 PyTorch License: MIT

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
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.

https://arxiv.org/abs/2102.11005
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

Official Implementation for
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

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

Non-Official Pytorch implementation of
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

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

PAWS-TF šŸ¾ Implementation of Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples (PAWS)

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

Introduction Yolov5-face is a real-time,high accuracy face detection. Performance Single Scale Inference on VGA resolutionļ¼ˆmax side is equal to 640 an

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

Spiking Neural Network training with EventProp This is an unofficial PyTorch implemenation of EventProp, a method to compute exact gradients for Spiki

Unofficial implementation of
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

Releases(v1.1.1)
Owner
Max Berrendorf
Max Berrendorf
Supplementary code for the paper "Meta-Solver for Neural Ordinary Differential Equations" https://arxiv.org/abs/2103.08561

Meta-Solver for Neural Ordinary Differential Equations Towards robust neural ODEs using parametrized solvers. Main idea Each Runge-Kutta (RK) solver w

Julia Gusak 25 Aug 12, 2021
Code for the paper: Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization (https://arxiv.org/abs/2002.11798)

Representation Robustness Evaluations Our implementation is based on code from MadryLab's robustness package and Devon Hjelm's Deep InfoMax. For all t

Sicheng 19 Dec 7, 2022
Source code for models described in the paper "AudioCLIP: Extending CLIP to Image, Text and Audio" (https://arxiv.org/abs/2106.13043)

AudioCLIP Extending CLIP to Image, Text and Audio This repository contains implementation of the models described in the paper arXiv:2106.13043. This

null 458 Jan 2, 2023
Official repository with code and data accompanying the NAACL 2021 paper "Hurdles to Progress in Long-form Question Answering" (https://arxiv.org/abs/2103.06332).

Hurdles to Progress in Long-form Question Answering This repository contains the official scripts and datasets accompanying our NAACL 2021 paper, "Hur

Kalpesh Krishna 41 Nov 8, 2022
Official implementation of the paper Image Generators with Conditionally-Independent Pixel Synthesis https://arxiv.org/abs/2011.13775

CIPS -- Official Pytorch Implementation of the paper Image Generators with Conditionally-Independent Pixel Synthesis Requirements pip install -r requi

Multimodal Lab @ Samsung AI Center Moscow 201 Dec 21, 2022
[PyTorch] Official implementation of CVPR2021 paper "PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency". https://arxiv.org/abs/2103.05465

PointDSC repository PyTorch implementation of PointDSC for CVPR'2021 paper "PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency",

null 153 Dec 14, 2022
This is an official implementation of our CVPR 2021 paper "Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression" (https://arxiv.org/abs/2104.02300)

Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression Introduction In this paper, we are interested in the bottom-up paradigm of estima

HRNet 367 Dec 27, 2022
Unofficial Tensorflow-Keras implementation of Fastformer based on paper [Fastformer: Additive Attention Can Be All You Need](https://arxiv.org/abs/2108.09084).

Fastformer-Keras Unofficial Tensorflow-Keras implementation of Fastformer based on paper Fastformer: Additive Attention Can Be All You Need. Tensorflo

Yam Peleg 10 Jan 30, 2022
This repository contains the code used for Predicting Patient Outcomes with Graph Representation Learning (https://arxiv.org/abs/2101.03940).

Predicting Patient Outcomes with Graph Representation Learning This repository contains the code used for Predicting Patient Outcomes with Graph Repre

Emma Rocheteau 76 Dec 22, 2022
source code for https://arxiv.org/abs/2005.11248 "Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics"

Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics This work will be published in Nature Biomedical

International Business Machines 71 Nov 15, 2022