# DKRL
**Repository Path**: xugus/DKRL
## Basic Information
- **Project Name**: DKRL
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-01-04
- **Last Updated**: 2022-01-04
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# DKRL
New: Add Evaluation code for DKRL(CNN)+TransE, additional TransE results are needed to run this evaluation.
"../transE_res/entity2vec."+version
"../transE_res/relation2vec."+version
with the same dimension and unif/bern.
# INTRODUCTION
Description-Embodied Knowledge Representation Learning (DKRL)
Representation Learning of Knowledge Graphs with Entity Descriptions (AAAI'16)
Ruobing Xie
# COMPILE
Just type make in the folder ./
# NOTE
Pre-trained embeddings for entity/relation/word are optional.
We update both Structure-based Representations and Description-based Representations in this version. We can also fix Structure-based Representations pre-trained by other models and only update Description-based Representations.
For DKRL, we learn structure-based representations (SBR) and description-based representations (DBR) simultaneously in training.
However, Test_cnn.cpp only use description-based representations for prediction. You can load in both entity representations for joint prediction.
# DATA
FB15k is published by the author of the paper "Translating Embeddings for Modeling Multi-relational Data (2013)."
[download]
You can also get FB15k from here: [download]
Entity list and descriptions of FB15k used in this work [download]
FB20k is based on FB15k and used for zero-shot scenario [download]
Entity type information for entity classification [download]
All these datasets are also in data.rar.
Entity name file [download]
# CITE
If the codes or datasets help you, please cite the following paper:
Ruobing Xie, Zhiyuan Liu, Jia Jia, Huanbo Luan, Maosong Sun. Representation Learning of Knowledge Graphs with Entity Descriptions. The 30th AAAI Conference on Artificial Intelligence (AAAI'16).