Code for paper PairRE: Knowledge Graph Embeddings via Paired Relation Vectors.

Last update: Dec 19, 2022

Overview

PairRE

Code for paper PairRE: Knowledge Graph Embeddings via Paired Relation Vectors.

This implementation of PairRE for Open Graph Benchmak datasets (ogbl-wikikg and ogbl-biokg) is based on OGB. Thanks for their contributions.

Setup

To run the code, you need the following dependencies:

Pytorch 1.4.0
ogb 1.2.2 for wikikg
ogb 1.2.4 for wikikg2

Results

The results of PairRE on ogbl-wikikg, ogbl-biokg and ogbl-wikikg2 are as follows.

ogbl-wikikg

	#Dim	#Parameters	Hardware	Test MRR	Valid MRR
PairRE	100	250,167,400	16GB GPU	0.4912 ± 0.004	0.5013 ± 0.004
PairRE	200	500,334,800	16GB GPU	0.5289 ± 0.003	0.5529 ± 0.001

ogbl-biokg

	#Dim	#Parameters	Hardware	Test MRR	Valid MRR
PairRE	2000	187,750,000	16GB GPU	0.8164 ± 0.0005	0.8172 ± 0.0005

ogbl-wikikg2

	#Dim	#Parameters	Hardware	Test MRR	Valid MRR
PairRE	100	250,167,400	16GB GPU	0.4849 ± 0.003	0.4941 ± 0.004
PairRE	200	500,334,800	16GB GPU	0.5208 ± 0.003	0.5423 ± 0.002

Running the code

ogbl-wikikg

cd wikikg && sh examples.sh

ogbl-biokg

cd biokg && sh examples.sh

ogbl-wikikg2

Please update ogb package to version 1.2.4. The hyperparameters are same to the experiments in ogbl-wikikg.

cd wikikg && sh examples.sh

The details of the optional hyperparameters can be found in examples.sh.

Code for paper PairRE: Knowledge Graph Embeddings via Paired Relation Vectors.

Related tags

Overview

PairRE

Setup

Results

ogbl-wikikg

ogbl-biokg

ogbl-wikikg2

Running the code

ogbl-wikikg

ogbl-biokg

ogbl-wikikg2

Owner

Alipay

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Code for paper PairRE: Knowledge Graph Embeddings via Paired Relation Vectors.

Related tags

Overview

PairRE

Setup

Results

ogbl-wikikg

ogbl-biokg

ogbl-wikikg2

Running the code

ogbl-wikikg

ogbl-biokg

ogbl-wikikg2

Owner

Alipay

Implementation of several Bayesian multi-target tracking algorithms, including Poisson multi-Bernoulli mixture filters for sets of targets and sets of trajectories. The repository also includes the GOSPA metric and a metric for sets of trajectories to evaluate performance.

ML powered analytics engine for outlier detection and root cause analysis.

NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions (CVPR2021)

Experiments and code to generate the GINC small-scale in-context learning dataset from "An Explanation for In-context Learning as Implicit Bayesian Inference"

Pytorch implementation of the paper SPICE: Semantic Pseudo-labeling for Image Clustering

Code to reproduce experiments in the paper "Explainability Requires Interactivity".

Brain Tumor Detection with Tensorflow Neural Networks.

This is the face keypoint train code of project face-detection-project

Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image

Release of the ConditionalQA dataset

Live Hand Tracking Using Python

Data stream analytics: Implement online learning methods to address concept drift in data streams using the River library. Code for the paper entitled "PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams" accepted in IEEE GlobeCom 2021.

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YOLOX-CondInst - Implement CondInst which is a instances segmentation method on YOLOX

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren*, Raymond A. Yeh*, Alexander G. Schwing.

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code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing.