From 004c027d289147e149e08ea177d65bdd16f08f5d Mon Sep 17 00:00:00 2001 From: Weiyu Liu Date: Fri, 31 Jul 2020 23:29:03 -0400 Subject: [PATCH] updated readme --- .gitignore | 2 +- README.md | 47 +++++++++++++++++++++++++++++++++++++-------- figure/figure1.png | Bin 0 -> 709847 bytes 3 files changed, 40 insertions(+), 9 deletions(-) create mode 100644 figure/figure1.png diff --git a/.gitignore b/.gitignore index abbb866..9a601f6 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,4 @@ __pycache__/ runs/ -data/ +!data/ .idea/ \ No newline at end of file diff --git a/README.md b/README.md index 150e367..2ed8b3f 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,8 @@ ## Path Ranking with Attention to Type Hierarchies -This repo contains code for training and testing the proposed models in *Path Ranking with Attention to Type Hierarchies*. -Due to its large size, data needs to be downloaded separately from [dropbox](https://www.dropbox.com/s/0a4o2jljg4imuux/data.zip?dl=0). +This repo contains code for AAAI 2020 paper *Path Ranking with Attention to Type Hierarchies*. +drawing -## Notes +## Resources 1. Code for baseline models in the paper can be found [here](https://github.com/matt-gardner/pra) (PRA and SFE) and [here](https://github.com/rajarshd/ChainsofReasoning) (Path-RNN). 2. We provide tokenized data for WN18RR and FB15k-237. Our data format follows @@ -23,18 +23,49 @@ FB15k-237 entities can be found [here](https://github.com/thunlp/TKRL). 3. Install pytorch with cuda 4. Install requirements by running `pip3 install -r requirements.txt` -## Instruction for running the code +## Quick Start for Attentive Path Ranking (APR) model +To help you quickly train and test the model, we have prepared data that are already vectorized. + ### Data -1. Compressed data file can be downloaded from [dropbox](https://www.dropbox.com/s/0a4o2jljg4imuux/data.zip?dl=0) +1. Vectorized data file can be downloaded from [dropbox](https://www.dropbox.com/s/0a4o2jljg4imuux/data.zip?dl=0). 2. Unzip the file in the root directory of this repo. ### Run the model -1. Use `run.py` to train and test the model on WN18RR or FB15k-237. -2. Use `/main/playground/model2/CompositionalVectorSpaceAlgorithm.py` to modify the training settings and hyperparamters. -3. Use `main/playground/model2/CompositionalVectorSpaceModel.py` to modify the network design. Different attention methods for +1. Use [`run.py`](run.py) to train and test the model on WN18RR or FB15k-237. +2. Use [`main/playground/model2/CompositionalVectorSpaceAlgorithm.py`](main/playground/model2/CompositionalVectorSpaceAlgorithm.py) to modify the training settings and hyperparamters. +3. Use [`main/playground/model2/CompositionalVectorSpaceModel.py`](main/playground/model2/CompositionalVectorSpaceAlgorithm.py) to modify the network design. Different attention methods for types and paths can be selected here. 4. Training progress can be monitored using tensorboardX by running `tensorboard --logdir runs`. Tutorials and Details can be found [here](https://github.com/lanpa/tensorboardX). +## Running the Complete Knowledge Graph Completion Pipeline +You can also use the code to build knowledge graphs from relation data, sample negative examples, create splits, +extract paths, vectorize path data, run baselines, and run APR model. + +### Data +1. Raw data, including relation data, type information, and word embeddings, for WN18RR, FB15k-237 can be downloaded +from [dropbox](). +2. Unzip the file in the root directory of this repo. + +### Install Dependencies +1. Clone our [PRA repo](https://github.com/wliu88/pra_scala) and install necessary dependencies. For more details, check +out readme in that repo. +2. (Optional) Clone our [CVSM repo](https://github.com/wliu88/ChainsofReasoning) only if you want to run CVSM baselines. + +### Run +1. Use [`demo_wn18rr.py`](demo_wn18rr.py) or [`demo_fb15k237.py`](demo_fb15k237.py) to go through the whole pipeline for the respective dataset. +2. Use [`run_wn18rr.py`](run_wn18rr.py) or [`run_fb15k237.py`](run_fb15k237.py) if you want to run PRA, SFE, and CVSM baselines + +## Code Documentation +Code are documented with docstrings. We also use `sphinx` to automatically generate API documentation from docstrings. + +### Viewing the documentation + +1. Make sure you have the necessary `pip` dependencies of `sphinx` installed. +1. Navigate to [`docs`](docs/) and build the documentation: `make html` +1. Navigate to [`docs/_build/html`](docs/_build/html) (this folder will be generated by the previous command) and +start a simple server: `python -m SimpleHTTPServer`. +1. Navigate to [`http://localhost:8000`](http://localhost:8000) in your browser. + ## Citation Please cite the following work if you find the data/code useful. ```text diff --git a/figure/figure1.png b/figure/figure1.png new file mode 100644 index 0000000000000000000000000000000000000000..105f7766770b2e0cc4774086e62f66ef91e10390 GIT binary patch literal 709847 zcmeFaXH=Bg(gvz1iYNk#q98#r0Rp1rEGVEv$r%Izp$U>Rh=@9pR3vA~Ip-(}C^>^f z0m(Twso~Z;-$xw?UV_ujRuo_eb4>EEO!MF{Z8@b~T8M{rm4 zw#>eL$BXvuBMinn1g{i@9=W=2-~KBGLPF9gArT={BU5wP#}9OLZ#^{C)|C+vWai}L z*thTM8yzhz8PRK3KR-C3rS-Xio)+KSM&{*9PZ=$j`id_VwH0+*6$*Zk-6+X;mF|I-R{g##zdA8|ff-6_)r+*` z^&0!MN%vFWJv7~)*>-;Bq!ugD8bwC%YifP!tpl$c0uJikbe$O%dy#=FV3bfwO+69# zHsW2X4 zv)w<_eMQyu=IG4Q10yp z+VMgSzjkqpd++?7uX8=XD|t$D!!5G@WBK^@pjd!IQ>lJL!>D<|cy<@p^bBT3pa0|d zS9GQM;;KRJwx$RN9uc*`lYRSf4*oCy!;&p9tv4rS^S}N5KRzBG=V_Y8!TiK&TwLU_$&0F3xAw=&ic97-bE&rK;w4 zJIt4kv_D*kKC6AAA zw*O*}QgtWEImhq3{i>E(QWm*h=s)b0G|hGEneJ|b_^{#k 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