🤖 Neural SPARQL Machines translate natural language into SPARQL queries.
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🤖 Neural SPARQL Machines

A LSTM-based Machine Translation Approach for Question Answering.

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Install git-lfs in your machine, then fetch all files and submodules.

git lfs fetch
git lfs checkout
git submodule update --init

Install TensorFlow (e.g., pip install tensorflow).

Data preparation


The template used in the paper can be found in a file such as annotations_monument.tsv. To generate the training data, launch the following command.

python generator.py --templates data/annotations_monument.csv  --output data/monument_300

Build the vocabularies for the two languages (i.e., English and SPARQL) with:

python build_vocab.py data/monument_300/data_300.en > data/monument_300/vocab.en
python build_vocab.py data/monument_300/data_300.sparql > data/monument_300/vocab.sparql

Count lines in data_.*

NUMLINES= $(echo awk '{ print $1}' | cat data/monument_300/data_300.sparql |  wc -l)
# 7097

Split the data_.* files into train_.*, dev_.*, and test_.* (usually 80-10-10%).

cd data/monument_300/
python ../../split_in_train_dev_test.py --lines $NUMLINES  --dataset data.sparql

Pre-generated data

Alternatively, you can extract pre-generated data from data/monument_300.zip and data/monument_600.zip in folders having the respective names.


Launch train.sh to train the model. The first parameter is the prefix of the data directory. The second parameter is the number of training epochs.

sh train.sh data/monument_300 120000

This command will create a model directory called data/monument_300_model.


Predict the SPARQL sentence for a given question with a given model.

sh ask.sh data/monument_300 "where is edward vii monument located in?"


    author = "Tommaso Soru and Edgard Marx and Diego Moussallem and Gustavo Publio and Andr\'e Valdestilhas and Diego Esteves and Ciro Baron Neto",
    title = "{SPARQL} as a Foreign Language",
    year = "2017",
    journal = "13th International Conference on Semantic Systems (SEMANTiCS 2017) - Posters and Demos",
    url = "http://w3id.org/neural-sparql-machines/soru-marx-semantics2017.html",