# MetaR-pytorch **Repository Path**: jinnyang/MetaR-pytorch ## Basic Information - **Project Name**: MetaR-pytorch - **Description**: 基于元学习的知识图谱补全，原代码优化与改进 - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2023-02-27 - **Last Updated**: 2024-04-28 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # MetaR This repo shows the source code of EMNLP 2019 paper: [**Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs**](https://www.aclweb.org/anthology/D19-1431/). 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 ```bash 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](https://github.com/xwhan/One-shot-Relational-Learning). You can also download the zip files where we put the datasets and pretrain embeddings together from [Dropbox](https://www.dropbox.com/sh/d04wbxx8g97g1rb/AABDZc-2pagoGhKzNvw0bG07a?dl=0). 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](https://github.com/xwhan/One-shot-Relational-Learning). Note that if you use our released datasets from [Dropbox](https://www.dropbox.com/sh/d04wbxx8g97g1rb/AABDZc-2pagoGhKzNvw0bG07a?dl=0), 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 ```bash 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 ```bash 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, ```bash # 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, ```bash --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 ```bash 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 ```bash 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 ```bash 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 ```Bash 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" } ```