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توضیحات

A Library for Dynamically Editing PLMs-Based Knowledge Graph Embeddings.

ویژگی	مقدار
سیستم عامل	-
نام فایل	DeltaKG-0.0.6
نام	DeltaKG
نسخه کتابخانه	0.0.6
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Bozhong Tian
ایمیل نویسنده	tbozhong@zju.edu.cn
آدرس صفحه اصلی	https://github.com/zjunlp/PromptKG/tree/main/deltaKG
آدرس اینترنتی	https://pypi.org/project/DeltaKG/
مجوز	-

<img src="https://github.com/zjunlp/PromptKG/blob/main/resources/deltakg_logo.png" width="550px"> A Library for Dynamically Editing PLMs-Based Knowledge Graph Embeddings. ------ <a href="#overview">Overview</a> • <a href="#installation">Installation</a> • <a href="#how-to-run">How To Run</a> • <a href="https://arxiv.org/pdf/2301.10405">Paper</a> • <a href="https://medium.com/@jack16900/deltakg-a-library-for-dynamically-editing-plm-based-kg-embeddings-243d59a8f168">Medium</a> • <a href="#how-to-cite">Citation</a> • <a href="#other-kg-representation-open-source-projects">Others</a> ## Overview Knowledge graph embedding (KGE) is a method for representing symbolic facts in low-dimensional vector spaces, with the goal of projecting relations and entities into a continuous vector space. This approach enhances knowledge reasoning capabilities and facilitates application to downstream tasks. We introduce DeltaKG (MIT License), a dynamic, PLM-based library for KGEs that equips with numerous baseline models, such as K-Adapter, CaliNet, KnowledgeEditor, MEND, and KGEditor, and supports a variety of datasets, including E-FB15k237, A-FB15k237, E-WN18RR, and A-WN18RR. **DeltaKG** is now publicly open-sourced, with [a demo](https://huggingface.co/spaces/zjunlp/KGEditor), [a leaderboard](https://zjunlp.github.io/project/KGE_Editing/) and long-term maintenance.  ## Model Architecture <img src="resource/model.png" width="75%" height="75%" /> Illustration of KGEditor for a) The external model-based editor, b) The additional parameter-based editor and c) KGEditor. ## Installation **Step1** Download the basic code ```bash git clone --depth 1 https://github.com/zjunlp/PromptKG.git ``` **Step2** Create a virtual environment using `Anaconda` and enter it ```bash conda create -n deltakg python=3.8 conda activate deltakg ``` **Step3** Enter the task directory and install library ```bash cd PromptKG/deltaKG pip install -r requirements.txt ``` ## Data & Checkpoints Download ### Data The datasets that we used in our experiments are as follows, - E-FB15k237 This dataset is based on FB15k237 and a pre-trained language-model-based KGE. You can download the E-FB15k237 dataset from [Google Drive](https://drive.google.com/drive/folders/1K1gag6eTCJ-x7WM-zbn6IRGX_B9fy4hD?usp=share_link). For other datasets `A-FB15k237`, `E-WN18RR`, and `A-WN18RR`, you can also download from the above link. ### Checkpoints The checkpoints that we used in our experiments are as follows, - PT_KGE_E-FB15k237 This checkpoint is based on FB15k237 and a pre-trained language model. You can download the PT_KGE_E-FB15k237 checkpoint from [Google Drive](https://drive.google.com/drive/folders/1EOHdg8rC9iwgSyKl5RnEv9z6ATW5Ntbr?usp=share_link). For other checkpoints `PT_KGE_A-FB15k237`, `PT_KGE_E-WN18RR`, and `PT_KGE_A-WN18RR`, you can also download from the above link. The expected structure of files is: ``` DeltaKG |-- checkpoints # checkpoints for tasks |-- datasets # task data | |-- FB15k237 # dataset's name | | |-- AddKnowledge # data for add task, A-FB15k237 | | | |-- train.jsonl | | | |-- dev.jsonl | | | |-- test.jsonl | | | |-- stable.jsonl | | | |-- relation2text.txt | | | |-- relation.txt | | | |-- entity2textlong.txt | | |-- EditKnowledge # data for edit task, E-FB15k237 | | | |-- ... # consistent with A-FB15k237 | |-- WN18RR # dataset's name | | |-- AddKnowledge # data for add task, A-WN18RR | | | |-- train.jsonl | | | |-- dev.jsonl | | | |-- test.jsonl | | | |-- stable.jsonl | | | |-- relation2text.txt | | | |-- relation.txt | | | |-- entity2text.txt | | |-- EditKnowledge # data for edit task, E-WN18RR | | | |-- ... # consistent with A-WN18RR |-- models # KGEditor and baselines | |-- CaliNet | | |-- run.py | | |-- ... | |-- K-Adapter | |-- KE # KnowledgeEditor | |-- KGEditor | |-- MEND |-- resource # image resource |-- scripts # running scripts | |-- CaliNet | | |-- CaliNet_FB15k237_edit.sh | | |-- ... | |-- K-Adapter | |-- KE # KnowledgeEditor | |-- KGEditor | |-- MEND |-- src | |-- data # data process functions | |-- models # source code of models |-- README.md |-- requirements.txt |-- run.sh # script to quick start ``` ## How to run - ### script - The script `run.sh` has three arguments `-m`, `-d`, and `-t`, which stand for model, dataset, and task. - `-m`: should be the name of a model in models (e.g. `KGEditor`, `MEND`, `KE`); - `-d`: should be either `FB15k237` or `WN18RR`; - `-t`: should be either `edit` or `add`. - ### Edit Task - To train the `KGEditor` model in the paper on the dataset `E-FB15k237`, run the command below. ```shell bash run.sh -m KGEditor -d FB15k237 -t edit ``` - To train the `KGEditor` model in the paper on the dataset `E-WN18RR`, run the command below. ```shell bash run.sh -m KGEditor -d WN18RR -t edit ``` - ### Add Task - To train the `KGEditor` model in the paper on the dataset `A-FB15k237`, run the command below. ```shell bash run.sh -m KGEditor -d FB15k237 -t add ``` - To train the `KGEditor` model in the paper on the dataset `A-WN18RR`, run the command below. ```shell bash run.sh -m KGEditor -d WN18RR -t add ``` ## Experiments Up to now, baseline models include K-Adapter, CaliNet, KE, MEND, and KGEditor. The results of these models are as follows, - E-FB15k237 |Model | $Succ@1$ | $Succ@3$ | $ER_{roc}$ | $RK@3$ | $RK_{roc}$ | |:-: |:-: |:-: |:-: |:-: |:-: | |Finetune |0.472|0.746|0.998|0.543|0.977| |Zero-Shot Learning |0.000|0.000|-|1.000|0.000| |K-Adapter |0.329|0.348|0.926|0.001|0.999| |CaliNet |0.328|0.348|0.937|0.353|0.997| |KE |0.702|0.969|0.999|0.912|0.685| |MEND |0.828|0.950|0.954|0.750|0.993| |KGEditor |0.866|0.986|0.999|0.874|0.635| - E-WN18RR |Model | $Succ@1$ | $Succ@3$ | $ER_{roc}$ | $RK@3$ | $RK_{roc}$ | |:-: |:-: |:-: |:-: |:-: |:-: | |Finetune |0.758|0.863|0.998|0.847|0.746| |Zero-Shot Learning |0.000|0.000|-|1.000|0.000| |K-Adapter |0.638|0.752|0.992|0.009|0.999| |CaliNet |0.538|0.649|0.991|0.446|0.994| |KE |0.599|0.682|0.978|0.935|0.041| |MEND |0.815|0.827|0.948|0.957|0.772| |KGEditor |0.833|0.844|0.991|0.956|0.256| - A-FB15k237 |Model | $Succ@1$ | $Succ@3$ | $ER_{roc}$ | $RK@3$ | $RK_{roc}$ | |:-: |:-: |:-: |:-: |:-: |:-: | |Finetune |0.906|0.976|0.999|0.223|0.997| |Zero-Shot Learning |0.000|0.000|-|1.000|0.000| |K-Adapter |0.871|0.981|0.999|0.000|0.999| |CaliNet |0.714|0.870|0.997|0.034|0.999| |KE |0.648|0.884|0.997|0.926|0.971| |MEND |0.517|0.745|0.991|0.499|0.977| |KGEditor |0.796|0.923|0.998|0.899|0.920| - A-WN18RR |Model | $Succ@1$ | $Succ@3$ | $ER_{roc}$ | $RK@3$ | $RK_{roc}$ | |:-: |:-: |:-: |:-: |:-: |:-: | |Finetune |0.997|0.999|0.999|0.554|0.996| |Zero-Shot Learning |0.000|0.000|-|1.000|0.000| |K-Adapter |0.898|0.978|0.999|0.002|0.999| |CaliNet |0.832|0.913|0.995|0.511|0.989| |KE |0.986|0.996|0.999|0.975|0.090| |MEND |0.999|1.0|0.999|0.810|0.987| |KGEditor |0.998|1.0|0.999|0.956|0.300| We are still trying different hyper-parameters and training strategies for these models, and may add new models to this table. We also provide a [leaderboard](https://zjunlp.github.io/project/KGE_Editing/) and a [demo](https://huggingface.co/spaces/zjunlp/KGEditor). ## Citation If you use or extend our work, please cite the paper as follows: ```bibtex @article{cheng2023editing, title={Editing Language Model-based Knowledge Graph Embeddings}, author={Cheng, Siyuan and Zhang, Ningyu and Tian, Bozhong and Dai, Zelin and Xiong, Feiyu and Guo, Wei and Chen, Huajun}, journal={arXiv preprint arXiv:2301.10405}, year={2023} } ``` ## Other KG Representation Open-Source Projects - [OpenKE](https://github.com/thunlp/OpenKE) - [LibKGE](https://github.com/uma-pi1/kge) - [CogKGE](https://github.com/jinzhuoran/CogKGE) - [PyKEEN](https://github.com/pykeen/pykeen) - [GraphVite](https://graphvite.io/) - [Pykg2vec](https://github.com/Sujit-O/pykg2vec) - [PyG](https://github.com/pyg-team/pytorch_geometric) - [CogDL](https://github.com/THUDM/cogdl) - [NeuralKG](https://github.com/zjukg/NeuralKG) - [KGxBoard](https://github.com/neulab/KGxBoard)

زبان مورد نیاز

مقدار	نام
>=3.6	Python

نحوه نصب

نصب پکیج whl DeltaKG-0.0.6:

pip install DeltaKG-0.0.6.whl

نصب پکیج tar.gz DeltaKG-0.0.6:

pip install DeltaKG-0.0.6.tar.gz