An Information Gain based Question Answering system over knowledge graph systems.
- chmod +x parallel_data_creation.sh
- download glove42B and save it in resource folder
- mkdir logs
- ./parallel_data_creation.sh
- python data_creation_step1.py
- python reduce_data_creation_step2.py
- CUDA_VISIBLE_DEVICES=3 python corechain.py -model slotptr -device cuda -dataset lcquad -pointwise False
wget http://nlp.stanford.edu/data/glove.42B.300d.zip save it to resource folder unzip it
conda env create -f environment.yml
For installation https://redis.io/topics/quickstart
@TODO: add code here
Install nodejs (node, nodejs)
> nodejs app.js
python ei_server.py (Keep this always on)
This will need bottle installed (pip install bottle)
Check for running verison of DBPedia, Redis (if caching), SPARQL Parsing server, Embedding interface
https://github.com/lukovnikov/qelos-util.git change into qelos-util dir and python setup.py build/develop/ cp qelos ../
A rdftype_lookup.json can be created using the keys of relation.pickle (data/data/common)
import numpy as np
mat = np.load('resources/vectors_gl.npy')
mat = mat.astype(np.float64)
np.save('resources/vectors_gl.npy',mat)
#### TODO
change embedding in configs to 300d
To check if all the files are in correct palce run the following command
python file_location_check.py
Once the data is at appropriate place run the following command.
CUDA_VISIBLE_DEVICES=3 python corechain.py -model slotptr -device cuda -dataset lcquad -pointwise False