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https://github.com/castorini/BuboQA
Simple question answering over knowledge graphs (Mohammed et al., NAACL 2018)
https://github.com/castorini/BuboQA
Last synced: 10 days ago
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Simple question answering over knowledge graphs (Mohammed et al., NAACL 2018)
- Host: GitHub
- URL: https://github.com/castorini/BuboQA
- Owner: castorini
- Created: 2017-06-18T17:16:42.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2019-05-28T10:25:41.000Z (over 5 years ago)
- Last Synced: 2024-08-02T08:09:54.467Z (3 months ago)
- Language: Python
- Homepage:
- Size: 349 KB
- Stars: 281
- Watchers: 18
- Forks: 88
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Simple Question Answering over Knowledge Graphs
This repo contains code for the following paper:
+ Salman Mohammed, Peng Shi, and Jimmy Lin. [Strong Baselines for Simple Question Answering over Knowledge Graphs with and without Neural Networks](http://aclweb.org/anthology/N18-2047). *Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)*, pages 291-296, May 2018, New Orleans, Louisiana.
## Running the Code
Install the following Python 3 packages:
+ PyTorch (version 0.4.0)
+ torchtext (version 0.2.3)
+ NLTK
+ NLTK data (tokenizers, stopwords list)
+ fuzzywuzzy
If you use PyTorch version 0.2.0, please checkout to
```
commit 34a71f29192ed57f83d8576002f2b540de7d722f
```
Run the setup script. This takes a long time. It fetches dataset, other files, processes them and creates indexes:
```
sh setup.sh
```
There are four main components to our formulation of the problem, as detailed in the paper: entity detection, entity linking, relation prediction and evidence integration. Each of these components is contained in a separate directory, with an associated README.
+ `entity_detection` and `relation_prediction` can be run independently.
+ `entity_detection` needs to be run before `entity_linking`.
+ `entity_linking` and `relation_prediction` needs to be run before `evidence_integration`.
## Running the Code with Docker (GPU, Ubuntu 16, Cuda 9.0 base)
- Make sure you have the Docker daemon running
- Build the image from Dockerfile
```
cp docker_files/Dockerfile_gpu Dockerfile
docker build -t buboqa .
```
- Run the Docker image on GPU with nvidia-docker installed. Notice that we are mounting the current directory so that data persists.
```
nvidia-docker run -it --rm \
-v "$(pwd)":/code \
buboqa
```
- OR ... Run the Docker image on CPU (not tested)
```
docker run -it --rm \
-v "$(pwd)":/code \
buboqa
```
- Exit shell when needed
```
$ exit
```