# IKRL

**Repository Path**: Answercy/IKRL

## Basic Information

- **Project Name**: IKRL
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-11-09
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# IKRL

Image-embodied Knowledge Representation Learning (IJCAI-2017)

New: Add dataset


# INTRODUCTION

Image-embodied Knowledge Representation Learning (IKRL)

Image-embodied Knowledge Representation Learning (IJCAI-2017)

Written by Ruobing Xie


# COMPILE 

Just type make in the folder ./


# DATA

We use a new dataset WN9-IMG, with triples extracted from WN18 and images extracted from ImageNet.

There are additional files needed in training, pre-training is optional:

1. image2vec_fc7.txt: image feature vector, pre-trained by AlexNet (fc7 layer)
2. (optional) entity2vec.unif / relation2vec.unif: entity & relation vector, pre-trained by TransE
3. (optional) image_mat.unif: image projection matrix, pre-trained by IKRL (AVG)


# RUN

train: time ./Train_transI -size 50 -margin 4 -method 0

test: ./Test unif


# CITE

If the codes or datasets help you, please cite the following paper:

Ruobing Xie, Zhiyuan Liu, Huanbo Luan, Maosong Sun. Image-embodied Knowledge Representation Learning. The 26th International Joint Conference on Artificial Intelligence (IJCAI'17).