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https://github.com/HKUST-KnowComp/SQE
https://github.com/HKUST-KnowComp/SQE
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/HKUST-KnowComp/SQE
- Owner: HKUST-KnowComp
- Created: 2023-06-14T23:20:35.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-08-10T23:48:01.000Z (over 1 year ago)
- Last Synced: 2024-08-02T19:37:42.276Z (5 months ago)
- Language: Python
- Size: 81.1 KB
- Stars: 9
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-logical-query - SQE - LSTM, Tree-RNN, BetaE, BiQE, ConE, FuzzQE, GQE, HypE, NerualMLP (Mixer), Query2Box, Query2Particles (:wrench: Implementations / Dataset tools)
README
# Sequential Query Encoding (SQE)
The official implementation for the paper Sequential Query Encoding For Complex Query Answering on Knowledge Graphs [[Paper]](https://arxiv.org/pdf/2302.13114.pdf).
The KG data we are using is from the KG reasoning repo from [here](http://snap.stanford.edu/betae/KG_data.zip). The data descriptions are here: https://github.com/snap-stanford/KGReasoning. Please put the downloaded files under ./KG_data directory.
The complex query dataset for our benchmark can be downloaded [here](https://hkustconnect-my.sharepoint.com/:u:/g/personal/tzhengad_connect_ust_hk/EXgjlrPJHadPhPDQCuVFy88B-BCkdNJc1Mu1rTBURpfysQ?e=wCEFuo)（52.9GB).
Some people experience difficulty in downloading large files from onedrive on the command line. [Here](https://sushantag9.medium.com/download-data-from-onedrive-using-command-line-d27196a676d9) is a tutorial on downloading onedrive files in the command line.
We provided a wide range of baselines with our codebase. For experiments, please check out example.sh for script format.
During the running process, you can monitor the training process via tensorboard with following commands:
tensorboard --logdir your_log_dir --port the_port_you_fancy
ssh -N -f -L localhost:port_number:localhost:port_number your_server_location
## Supported Models:
Iterative Encoding Model:
| Model Flag (-m) | Paper |
|---|---|
| gqe | [Embedding logical queries on knowledge graphs](https://proceedings.neurips.cc/paper/2018/hash/ef50c335cca9f340bde656363ebd02fd-Abstract.html) |
| q2b | [Query2box: Reasoning over knowledge graphs in vector space using box embeddings](https://openreview.net/forum?id=BJgr4kSFDS) |
| betae | [Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs](https://proceedings.neurips.cc/paper/2020/hash/e43739bba7cdb577e9e3e4e42447f5a5-Abstract.html) |
| hype | [Self-supervised hyperboloid representations from logical queries over knowledge graphs](https://dl.acm.org/doi/10.1145/3442381.3449974) |
| mlp / mlp_mixer| [Neural methods for logical reasoning over knowledge graphs](https://openreview.net/forum?id=tgcAoUVHRIB) |
| cone | [Cone: Cone embeddings for multihop reasoning over knowledge graphs](https://openreview.net/pdf?id=Twf_XYunk5j) |
| q2p | [Query2Particles: Knowledge Graph Reasoning with Particle Embeddings](https://aclanthology.org/2022.findings-naacl.207/) |
| fuzzqe | [Fuzzy Logic Based Logical Query Answering on Knowledge Graphs](https://arxiv.org/abs/2108.02390) |
| tree_lstm | (this paper) [Sequential Query Encoding for Complex Query Answering on Knowledge Graphs](https://openreview.net/pdf?id=ERqGqZzSu5) |
| tree_rnn | (this paper) [Sequential Query Encoding for Complex Query Answering on Knowledge Graphs](https://openreview.net/pdf?id=ERqGqZzSu5) |
Sequential Encoding Models:
| Model Flag (-m) | Paper |
|---|---|
| biqe | [Answering Complex Queries in Knowledge Graphs with Bidirectional Sequence Encoders](https://arxiv.org/abs/2004.02596) |
| tcn | (this paper) [Sequential Query Encoding for Complex Query Answering on Knowledge Graphs](https://openreview.net/pdf?id=ERqGqZzSu5) |
| lstm | (this paper) [Sequential Query Encoding for Complex Query Answering on Knowledge Graphs](https://openreview.net/pdf?id=ERqGqZzSu5) |
| gru | (this paper) [Sequential Query Encoding for Complex Query Answering on Knowledge Graphs](https://openreview.net/pdf?id=ERqGqZzSu5) |
| transformer | (this paper) [Sequential Query Encoding for Complex Query Answering on Knowledge Graphs](https://openreview.net/pdf?id=ERqGqZzSu5) |
## Brining your own Query Encoding Model!
Also, you are welcome to build your own models with our benchmark, by overriding the functions in ./models/model.py. You only need to write your model, and the rest of things can be done by the code in this repo~
## Citations:
If you find the code/data/paper interesting, please cite our paper!
```
@article{
bai2023sequential,
title={Sequential Query Encoding for Complex Query Answering on Knowledge Graphs},
author={Jiaxin Bai and Tianshi Zheng and Yangqiu Song},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2023},
url={https://openreview.net/forum?id=ERqGqZzSu5},
note={}
}
```