# PathCon

**Repository Path**: yyx1/PathCon

## Basic Information

- **Project Name**: PathCon
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2020-12-01
- **Last Updated**: 2021-01-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# PathCon

This repository is the implementation of PathCon ([arXiv](https://arxiv.org/abs/2002.06757)):
> Entity Context and Relational Paths forKnowledge Graph Completion  
Hongwei Wang, Hongyu Ren, Jure Leskovec  
arXiv Preprint, 2020

![](https://github.com/hwwang55/PathCon/blob/master/model.png)

PathCon considers relational context and relational paths of (head, tail) pair in knowledge graphs for link prediction.
PathCon achieves substantial gains over state-of-the-art baselines.
Below is the result of Hit@1 on the test set of FB15K, FB15K-237, WN18, WN18RR, NELL995, and DDB14 datasets for relation prediction task:

| Method      | FB15K | FB15K-237 | WN18  | WN18RR | NELL995 | DDB14 |
| :---------: | :---: | :------:  | :---: | :----: | :----:  | :---: |
| TransE      | 94.0  | 94.6      | 95.5  | 66.9   | 78.1    | 94.8  |
| RotatE      | 96.7  | 95.1      | 97.9  | 73.5   | 69.1    | 93.4  |
| QuatE       | 97.2  | 95.8      | 97.5  | 76.7   | 70.6    | 92.2  |
| DRUM        | 94.5  | 90.5      | 95.6  | 77.8   | 64.0    | 93.0  |
| __PathCon__ | __97.4 (+/-0.2)__ | __96.4 (+/-0.1)__ | __98.8 (+/-0.1)__ | __95.4 (+/-0.2)__ | __84.4 (+/-0.4)__ | __96.6 (+/-0.1)__ |

For more results, please refer to the original paper.

### Files in the folder

- `data/`
  - `FB15k/`
  - `FB15k-237/`
  - `wn18/`
  - `wn18rr/`
  - `NELL995/`
  - `DDB14/`
- `src/`: implementation of PathCon.

__Note__: We provide a `cache/` folder for each dataset, which caches the pre-computed relational paths for this dataset.
This folder is not required for running the code because relational paths will be counted (and cached) if no corresponding cache file is found. 
**If you are going to run FB15K-237 with max_path_len=3, please first download and unzip ``paths_3.zip`` from [here](https://drive.google.com/file/d/1uF42OgIQY0f_G8z0Wwk90AQ_KEueqhsv/view?usp=sharing) and put all unzipped files under ``FB15k-237/cache/``** (the original files cannot be uploaded to GitHub due to the limitation on file size).
Again, this is not required but highly recommended because counting length-3 paths for FB15K-237 will be time-consuming.



### Running the code

```
$ python main.py
```
__Note__: The default dataset is set as WN18RR.
Hyper-parameter settings for other datasets are provided in  `main.py`.


### Required packages

The code has been tested running under Python 3.6.5, with the following packages installed (along with their dependencies):

- tensorflow == 1.12.0
- numpy == 1.16.5
- scipy == 1.3.1
- sklearn == 0.21.3