MetaR

This repo shows the source code of EMNLP 2019 paper: Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs. In this work, we propose a Meta Relational Learning (MetaR) framework to do the common but challenging few-shot link prediction in KGs, namely predicting new triples about a relation by only observing a few associative triples.

Running the Experiments

Requirements

• Python 3.6.7
• PyTorch 1.0.1
• tensorboardX 1.8

You can also install dependencies by

pip install -r requirements.txt

Dataset

We use NELL-One and Wiki-One to test our MetaR, and these datasets were firstly proposed by xiong. The orginal datasets and pretrain embeddings can be downloaded from xiong's repo. You can also download the zip files where we put the datasets and pretrain embeddings together from Dropbox. Note that all these files were provided by xiong and we just select what we need here.

Prepare

Here is a preparation step if you use the orginal datasets and embeddings from xiong's repo. Note that if you use our released datasets from Dropbox, just skip this step and the datasets do not need any preparation.

This preparation is mainly for forming the data of BG: In-Train setting, and unifies some file formats. Firstly, copy the corresponding embedding file Entity2vec.TransE to dataset folders, like ./NELL or ./Wiki . Secondly, prepare the datasets for BG: In-Train setting which we introduce in our paper by running

python prepare.py --dataset_path {path_of_dataset} --data_name {name_of_dataset}

• {path_of_dataset} specify the dataset path like ./NELL, ./Wiki;

• {name_of_dataset} specify the corresponding dataset name like NELL-One, Wiki-One.

For example

python prepare.py --data_path ./NELL --data_name NELL-One
python prepare.py --data_path ./Wiki --data_name Wiki-One

This script forms the data for BG: In-Train setting from original datasets, and you only need to run this script once before you running the following experiments.

Quick Start for Training & Testing

For training and testing MetaR, here is an example for queick start,

# NELL-One, 1-shot, BG:Pre-Train
python main.py --dataset NELL-One --data_path ./NELL --few 1 --data_form Pre-Train --prefix nellone_1shot_pretrain --device 0

Here are explanations of some important args,

--dataset:   "the name of dataset, NELL-One or Wiki-One"
--data_path: "directory of dataset"
--few:       "the number of few in {few}-shot, as well as instance number in support set"
--data_form: "dataset setting, Pre-Train or In-Train"
--prefix:    "given name of current experiment"
--device:    "the GPU number"

Normally, other args can be set to default values. See params.py for more details about argus if needed.

More Details

Log and State

Folder ./log and ./state will be make after starting an expariment. The log and a whole state of MetaR will be saved at {--log_dir}/{--prefix} and {--state_dir}/{--prefix} each {--eval_epoch} and {--checkpoint_epoch}, and their default values are set to 1000. For example, if current experiment prefix is exp1, here is the directory tree for log and state,

.
|-- log
|   \-- exp1
|       |-- events.out.tfevents.{num}.{username}  # tensorboard log
|       \-- res.log  # evaluation log during training and test log from logging module 
\-- state
    \-- exp1
        |-- checkpoint  # saved state every checkpoint_epoch
        \-- state_dict  # final state

The evaluation results and fianl results on dev set and test set will be logged at {--log_dir}/{--prefix}/res.log.

TensorboardX

tensorboard --logdir {log_dir}

log_dir is the directory of log which is specified by --log_dir when running main.py, and its default values is ./log.

Experiment Results

Under the default setting of parameters in the source code, we can get the following results. Corresponding command lines for running experiments are listed.

• NELL-One

  • 1-Shot Link Prediction

    python main.py --dataset NELL-One --data_path ./NELL --prefix nellone_1shot_pretrain --device 0
    python main.py --dataset NELL-One --data_path ./NELL --data_form In-Train --prefix nellone_1shot_intrain --device 0
    

    Model (Setting)	MRR	Hits@10	Hits@5	Hits@1
    MetaR (BG:Pre-Train)	.225	.350	.286	.159
    MetaR (BG:In-Train)	.308	.475	.406	.216

  • 5-Shot Link Prediction

    python main.py --dataset NELL-One --data_path ./NELL --few 5 --prefix nellone_5shot_pretrain --device 0
    python main.py --dataset NELL-One --data_path ./NELL --few 5 --data_form In-Train --prefix nellone_5shot_intrain --device 0
    

    Model (Setting)	MRR	Hits@10	Hits@5	Hits@1
    MetaR (BG:Pre-Train)	.238	.375	.321	.163
    MetaR (BG:In-Train)	.313	.499	.420	.214

• Wiki-One

  • 1-Shot Link Prediction

    python main.py --dataset Wiki-One --data_path ./Wiki --prefix wikione_1shot_pretrain --device 0
    python main.py --dataset Wiki-One --data_path ./Wiki --data_form In-Train --prefix wikione_1shot_intrain --device 0
    

    Model (Setting)	MRR	Hits@10	Hits@5	Hits@1
    MetaR (BG:Pre-Train)	.323	.414	.373	.275
    MetaR (BG:In-Train)	.189	.286	.234	.142

  • 5-Shot Link Prediction

    python main.py --dataset Wiki-One --data_path ./Wiki --few 5 --prefix wikione_5shot_pretrain --device 0
    python main.py --dataset Wiki-One --data_path ./Wiki --few 5 --data_form In-Train --prefix wikione_5shot_intrain --device 0
    

    Model (Setting)	MRR	Hits@10	Hits@5	Hits@1
    MetaR (BG:Pre-Train)	.333	.446	.396	.277
    MetaR (BG:In-Train)	.228	.321	.273	.182

Cite

@inproceedings{chen-etal-2019-meta,
    title     = "Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs",
    author    = "Chen, Mingyang  and
                 Zhang, Wen  and
                 Zhang, Wei  and
                 Chen, Qiang  and
                 Chen, Huajun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month     = nov,
    year      = "2019",
    address   = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url       = "https://www.aclweb.org/anthology/D19-1431",
    pages     = "4208--4217"
}