pytorch implementation of StreamE

Paper: "StreamE: Learning to Update Representations for Temporal Knowledge Graphs in Streaming Scenarios"

In this paper, we propose a lightweight framework called StreamE for the efficient generation of TKG representations in streaming scenarios. To reduce the parameter size, entity representations in StreamE are decoupled from the model training to serve as the memory module to store the historical information of entities. To achieve efficient update and generation, the process of generating representations is decoupled as two functions in StreamE. An update function is learned to incrementally update entity representations based on the newly-arrived knowledge and a read function is learned to predict the future semantics of entity representations. The Mindspore version implementation of our code is in https://github.com/zjs123/StreamE_MindSpore.

If you make use of this code in your work, please cite the following paper:

@inproceedings{DBLP:conf/sigir/ZhangS023,
  author       = {Jiasheng Zhang, Jie Shao, and, Bin Cui},
  title        = {StreamE: Learning to Update Representations for Temporal Knowledge
                  Graphs in Streaming Scenarios},
  booktitle    = {SIGIR 2023},
  pages        = {622--631},
  year         = {2023}
}

Contents

1. Installation
2. Train_and_Test
3. Datasets
4. Baselines
5. Contact

Installation

Install the following packages:

pip install torch
pip install numpy

Install CUDA and cudnn. Then run:

pip install cutorch
pip install cunn
pip install cudnn

Then clone the repository::

git clone https://github.com/zjs123/StreamE.git

We use Python3 for data processing and our code is also written in Python3.

Train_and_Test

The user can use the following command to reproduce the reported results of our models, in which the train and the evaluation processes are performed automatically.

python Main.py -dataset ICEWS14

Some of the important available options include:

        '-hidden', default = 100, type = int, help ='dimension of the learned embedding'
	'-lr',  default = 0.001, type = float, help = 'Learning rate'
	'-ns', 	 default = 10,   	type=int, 	help='negative samples for training'
	'-dataset', 	 default = "ICEWS14",   	choice=["ICEWS14","ICEWS05","GDELT"], 	help='dataset used to train'
	'-numOfEpoch', 	default=300,	type=int	help='Train Epoches'

Datasets

There are four datasets used in our experiment:ICEWS14, ICEWS05-15, ICEWS18, and GDELT. facts of each datases are formed as "[subject entity, relation, object entity, time]". Each data folder has four files:

-train.txt, test.txt, valid.txt: the first column is index of subject entity, second column is index of relation, third column is index of object entity, fourth column is the happened time of fact.

-stat.txt: num of entites and num of relations

The detailed statistic of each dataset

Datasets	Num of Entity	Num of Relation	Num of Time	Train	Valid	Test
ICEWS14 (Alberto et al., 2018)	7,128	230	365	72,826	8,941	8,963
ICEWS05-15 (Alberto et al., 2018)	10,488	251	4,071	38,6962	46,275	46,092
ICEWS18 (Zhen Han et al., 2020)	23,033	256	304	373,018	45,995	49,545
GDELT (Goel et al., 2018)	500	20	366	2,735,685	341,961	341,961

Baselines

We use following public codes for baseline experiments.

Baselines	Code	Embedding size	Batch num
TransE (Bordes et al., 2013)	Link	100, 200	100, 200
TTransE (Leblay et al., 2018)	Link	50, 100, 200	100, 200
TA-TransE (Alberto et al., 2018)	Link	100, 200	Default
HyTE (Dasgupta et al., 2018)	Link	Default	Default
DE-DistMult (Goel et al., 2020)	Link	Default	Default
TNTComplEX (Timothee et al., 2020)	Link	Default	Default
ATiSE (Chenjin et al., 2020)	Link	Default	Default

Contact

For any questions or suggestions you can use the issues section or contact us at zjss12358@gmail.com.