https://github.com/HKUST-KnowComp/GeoAlign

Source code for AKBC 2021 paper "Manifold Alignment across Geometric Spaces for Knowledge Base Representation Learning"
https://github.com/HKUST-KnowComp/GeoAlign

Last synced: about 1 month ago
JSON representation

Source code for AKBC 2021 paper "Manifold Alignment across Geometric Spaces for Knowledge Base Representation Learning"

• Host: GitHub
• URL: https://github.com/HKUST-KnowComp/GeoAlign
• Owner: HKUST-KnowComp
• License: mit
• Created: 2021-09-16T06:31:27.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-10-24T16:55:41.000Z (about 3 years ago)
• Last Synced: 2024-08-05T03:01:49.807Z (5 months ago)
• Language: Python
• Size: 36.6 MB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-taxonomy - https://github.com/HKUST-KnowComp/GeoAlign

README

        
# GeoAlign

Source code for AKBC 2021 paper [Manifold Alignment across Geometric Spaces for Knowledge Base Representation Learning](https://www.akbc.ws/2021/assets/pdfs/TPymTKJR-Pi.pdf).

### Requirements

* python: 3.6

* torch: 1.0.0

* scikit-learn

* pandas

* cython

* tqdm

* numpy

The hyperbolic embeddings in this repository (the poincare-embeddings folder) is inherited from the [poincare-embeddings repository](https://github.com/facebookresearch/poincare-embeddings).

## Data

We construct two taxonomies (YAGOwordnet and wikiObjects) and one knowledge graph (YAGOfacts) from [YAGO3](https://yago-knowledge.org/downloads/yago-3). Please refer to our paper for the data construction details.

#### Taxonomy data

* taxonomy.csv: This file contains the edges (i.e., the hypernymy relations) of the taxonomy.

* full_taxonomy.csv: This file contains the edges of the full transitive closure of the taxonomy, which is used in our experiments.

* full_transitive.txt / basic_edges.txt: The edges in the full transitive closure of the taxonomy / the transitive reduction of the taxonomy.

* types.txt / entities.txt / taxonomy_nodes.txt: The types / entities / union of types and entities in the taxonomy.

* taxonomy: The folder contains the training set and test set under different training rates.

#### Knowledge graph data

* TransE_KG: The folder contains the pretrained TransE embeddings of YAGOfacts named 'TransE.ckpt'. Please obtain a pairwise distance matrix of the entity embeddings and store it in this folder before running the code.

## Usage

To run the whole framework, run:

```

zsh run_all.sh

```

or

```

echo $Training_Rate"\n"$Data"\n"$Dimension"\n"$Hyperbolic_Model"\n" | xargs -L 4 -P $PARALLEL_R bash hyper_label_rate.sh

```

where $Training_Rate = {1, 2, 3, 4, 5}; $Data = {YAGOwordnet, wikiObjects}; $Dimension is the embedding dimension of the hyperbolic space; $Hyperbolic_Model = {lorentz, poincare}; $PARALLEL_R is the number of parallel programs.

More parameters can be edited in constants.sh.

### Citation

If you find this repository useful for your research, please kindly cite our paper:

```angular2

@inproceedings{

xiao2021manifold,

title={Manifold Alignment across Geometric Spaces for Knowledge Base Representation Learning},

author={Huiru Xiao and Yangqiu Song},

booktitle={3rd Conference on Automated Knowledge Base Construction},

year={2021},

url={https://openreview.net/forum?id=TPymTKJR-Pi}

}

```