# tkbc

**Repository Path**: songjh623/tkbc

## Basic Information

- **Project Name**: tkbc
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-11-28
- **Last Updated**: 2024-10-24

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Knowledge Base Completion (kbc)
This code reproduces results in [Tensor Decompositions for Temporal Knowledge Base Completion](https://arxiv.org/abs/2004.04926) (ICLR 2020).

## Installation
Create a conda environment with pytorch and scikit-learn :
```
conda create --name tkbc_env python=3.7
source activate tkbc_env
conda install --file requirements.txt -c pytorch
```

Then install the kbc package to this environment
```
python setup.py install
```

## Datasets

To download the datasets, go to the tkbc/scripts folder and run:
```
chmod +x download_data.sh
./download_data.sh
```

Once the datasets are downloaded, add them to the package data folder by running :
```
python tkbc/process_icews.py
python tkbc/process_yago.py
python tkbc/process_wikidata.py  # about 3 minutes.
```

This will create the files required to compute the filtered metrics.

## Reproducing results

In order to reproduce the results on the smaller datasets in the paper, run the following commands

```
python tkbc/learner.py --dataset ICEWS14 --model TNTComplEx --rank 156 --emb_reg 1e-2 --time_reg 1e-2

python tkbc/learner.py --dataset ICEWS05-15 --model TNTComplEx --rank 128 --emb_reg 1e-3 --time_reg 1

python tkbc/learner.py --dataset yago15k --model TNTComplEx --rank 189 --no_time_emb --emb_reg 1e-2 --time_reg 1
```




## License
tkbc is CC-BY-NC licensed, as found in the LICENSE file.