An Information Gain based Question Answering system over knowledge graph systems.

1. chmod +x parallel_data_creation.sh
2. download glove42B and save it in resource folder
3. mkdir logs
4. ./parallel_data_creation.sh
5. python data_creation_step1.py
6. python reduce_data_creation_step2.py
7. 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