RMNA: A Neighbor Aggregation-Based Knowledge Graph Representation Learning Model Using Rule Mining

Related tags

Deep LearningRMNA
Overview

RMNA: A Neighbor Aggregation-Based Knowledge Graph Representation Learning Model Using Rule Mining

Our code is based on Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

This README is also based on it.

This repository contains a Pytorch implementation of RMNA. We use AMIE to obtains horn rules. RMNA is a hierarchical neighbor aggregation model, which transforms valuable multi-hop neighbors into one-hop neighbors that are semantically similar to the corresponding multi-hop neighbors, so that the completeness of multi-hop neighbors can be ensured.

Requirements

Please download miniconda from above link and create an environment using the following command:

    conda env create -f pytorch35.yml

Activate the environment before executing the program as follows:

    source activate pytorch35

Dataset

We used two datasets for evaluating our model. All the datasets and their folder names are given below.

  • Freebase: FB15k-237
  • Wordnet: WN18RR

Rule Mining and Filtering

In the AMINE+ folder, we can generate mining rules by using the following command:

    java -jar amie_plus.jar [TSV file]

Without additional arguments AMIE+ thresholds using PCA confidence 0.1 and head coverage 0.01. You can change these default settings. See AMIE. The available files generated and processed by AMIE are placed in the folder of the corresponding dataset named new_triple.

Training

Parameters:

--data: Specify the folder name of the dataset.

--epochs_gat: Number of epochs for gat training.

--epochs_conv: Number of epochs for convolution training.

--lr: Initial learning rate.

--weight_decay_gat: L2 reglarization for gat.

--weight_decay_conv: L2 reglarization for conv.

--get_2hop: Get a pickle object of 2 hop neighbors.

--use_2hop: Use 2 hop neighbors for training.

--partial_2hop: Use only 1 2-hop neighbor per node for training.

--output_folder: Path of output folder for saving models.

--batch_size_gat: Batch size for gat model.

--valid_invalid_ratio_gat: Ratio of valid to invalid triples for GAT training.

--drop_gat: Dropout probability for attention layer.

--alpha: LeakyRelu alphas for attention layer.

--nhead_GAT: Number of heads for multihead attention.

--margin: Margin used in hinge loss.

--batch_size_conv: Batch size for convolution model.

--alpha_conv: LeakyRelu alphas for conv layer.

--valid_invalid_ratio_conv: Ratio of valid to invalid triples for conv training.

--out_channels: Number of output channels in conv layer.

--drop_conv: Dropout probability for conv layer.

How to run

When running for first time, run preparation script with:

    $ sh prepare.sh
  • Freebase

      $ python3 main.py --data ./data/FB15k-237/ --epochs_gat 2000 --epochs_conv 150  --get_2hop True --partial_2hop True --batch_size_gat 272115 --margin 1 --out_channels 50 --drop_conv 0.3 --output_folder ./checkpoints/fb/out/
    
  • Wordnet

      $ python3 main.py --data ./data/WN18RR/--epochs_gat 3600 --epochs_conv 150 --get_2hop True --partial_2hop True
    
Owner
宋朝都
宋朝都
An image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testingAn image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testing

SVM Données Une base d’images contient 490 images pour l’apprentissage (400 voitures et 90 bateaux), et encore 21 images pour fait des tests. Prétrait

Achraf Rahouti 3 Nov 30, 2021
Keyword spotting on Arm Cortex-M Microcontrollers

Keyword spotting for Microcontrollers This repository consists of the tensorflow models and training scripts used in the paper: Hello Edge: Keyword sp

Arm Software 1k Dec 30, 2022
[ICCV 2021 Oral] SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer

This repository contains the source code for the paper SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer (ICCV 2021 Oral). The project page is here.

AllenXiang 65 Dec 26, 2022
This repository is a series of notebooks that show solutions for the projects at Dataquest.io.

Dataquest Project Solutions This repository is a series of notebooks that show solutions for the projects at Dataquest.io. Of course, there are always

Dataquest 1.1k Dec 30, 2022
The official codes of our CVPR2022 paper: A Differentiable Two-stage Alignment Scheme for Burst Image Reconstruction with Large Shift

TwoStageAlign The official codes of our CVPR2022 paper: A Differentiable Two-stage Alignment Scheme for Burst Image Reconstruction with Large Shift Pa

Shi Guo 32 Dec 15, 2022
This repository contains the source code for the paper Tutorial on amortized optimization for learning to optimize over continuous domains by Brandon Amos

Tutorial on Amortized Optimization This repository contains the source code for the paper Tutorial on amortized optimization for learning to optimize

Meta Research 144 Dec 26, 2022
[ICCV 2021] Encoder-decoder with Multi-level Attention for 3D Human Shape and Pose Estimation

MAED: Encoder-decoder with Multi-level Attention for 3D Human Shape and Pose Estimation Getting Started Our codes are implemented and tested with pyth

ZiNiU WaN 176 Dec 15, 2022
[ICCV 2021] A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation

[ICCV 2021] A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation

CodingMan 45 Dec 12, 2022
Generate vibrant and detailed images using only text.

CLIP Guided Diffusion From RiversHaveWings. Generate vibrant and detailed images using only text. See captions and more generations in the Gallery See

Clay M. 401 Dec 28, 2022
CT-Net: Channel Tensorization Network for Video Classification

[ICLR2021] CT-Net: Channel Tensorization Network for Video Classification @inproceedings{ li2021ctnet, title={{\{}CT{\}}-Net: Channel Tensorization Ne

33 Nov 15, 2022
Code for "Contextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search"

Contextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search This is an implementation for our paper Contextual Non-Loca

Tencent YouTu Research 50 Dec 03, 2022
Official Repo of my work for SREC Nandyal Machine Learning Bootcamp

About the Bootcamp A 3-day Machine Learning Bootcamp organised by Department of Electronics and Communication Engineering, Santhiram Engineering Colle

MS 1 Nov 29, 2021
A simple Rock-Paper-Scissors game using CV in python

ML18_Rock-Paper-Scissors-using-CV A simple Rock-Paper-Scissors game using CV in python For IITISOC-21 Rules and procedure to play the interactive game

Anirudha Bhagwat 3 Aug 08, 2021
Code release for "Masked-attention Mask Transformer for Universal Image Segmentation"

Mask2Former: Masked-attention Mask Transformer for Universal Image Segmentation Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Ro

Meta Research 1.2k Jan 02, 2023
Official code of our work, AVATAR: A Parallel Corpus for Java-Python Program Translation.

AVATAR Official code of our work, AVATAR: A Parallel Corpus for Java-Python Program Translation. AVATAR stands for jAVA-pyThon progrAm tRanslation. AV

Wasi Ahmad 26 Dec 03, 2022
Code for DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents

DeepXML Code for DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents Architectures and algorithms DeepXML supports

Extreme Classification 49 Nov 06, 2022
[CVPR 2021] Unsupervised Degradation Representation Learning for Blind Super-Resolution

DASR Pytorch implementation of "Unsupervised Degradation Representation Learning for Blind Super-Resolution", CVPR 2021 [arXiv] Overview Requirements

Longguang Wang 318 Dec 24, 2022
End-to-end beat and downbeat tracking in the time domain.

WaveBeat End-to-end beat and downbeat tracking in the time domain. | Paper | Code | Video | Slides | Setup First clone the repo. git clone https://git

Christian J. Steinmetz 60 Dec 24, 2022
This is the official PyTorch implementation for "Mesa: A Memory-saving Training Framework for Transformers".

A Memory-saving Training Framework for Transformers This is the official PyTorch implementation for Mesa: A Memory-saving Training Framework for Trans

Zhuang AI Group 105 Dec 06, 2022
Sequential GCN for Active Learning

Sequential GCN for Active Learning Please cite if using the code: Link to paper. Requirements: python 3.6+ torch 1.0+ pip libraries: tqdm, sklearn, sc

45 Dec 26, 2022