A Machine-Translation Approach for Question Answering over Knowledge Graphs.
If you are looking for the code for papers "SPARQL as a Foreign Language" and "Neural Machine Translation for Query Construction and Composition" please checkout tag v0.1.0-akaha or branch v1.
Coming soon!
Clone the repository.
pip install -r requirements.txt
You can extract pre-generated data and model checkpoints from here (1.1 GB) in folders having the respective names.
The template used in the paper can be found in a file such as Annotations_F30_art.csv
. data/art_30
will be the ID of the working dataset used throughout the tutorial. To generate the training data, launch the following command.
mkdir -p data/art_30
python nspm/generator.py --templates data/templates/Annotations_F30_art.csv --output data/art_30
Launch the command if you want to build dataset seprately else it will internally be called while training.
python nspm/data_gen.py --input data/art_30 --output data/art_30
Now go back to the initial directory and launch learner.py
to train the model.
python nspm/learner.py --input data/art_30 --output data/art_30
This command will create a model checkpoints in data/art_30
and some pickle files in data/art_30/pickle_objects
.
Predict the SPARQL query for a given question it will store the detailed output in output_query.
python nspm/interpreter.py --input data/art_30 --output data/art_30 --query "yuncken freeman has architected in how many cities?"
or, if you want to use NSpM with airml to install pre-trained models, follow these steps,
- Install airML latest version from here
- Navigate to the table.kns here and check if your model is listed in that file.
- Then copy the name of that model and use it with the
interpreter.py
as follows
python interpreter.py --airml http://nspm.org/art --output data/art_30 --inputstr "yuncken freeman has architected in how many cities?"
- Components of the Adam Medical platform partly developed by Jose A. Alvarado at Graphen (including a humanoid robot called Dr Adam), rely on NSpM technology.
- The Telegram NSpM chatbot offers an integration of NSpM with the Telegram messaging platform.
- The Google Summer of Code program has been supporting 6 students to work on NSpM-backed project "A neural question answering model for DBpedia" since 2018.
- A question answering system was implemented on top of NSpM by Muhammad Qasim.
@inproceedings{soru-marx-2017,
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 = "https://arxiv.org/abs/1708.07624",
}
- NAMPI Website: https://uclnlp.github.io/nampi/
- arXiv: https://arxiv.org/abs/1806.10478
@inproceedings{soru-marx-nampi2018,
author = "Tommaso Soru and Edgard Marx and Andr\'e Valdestilhas and Diego Esteves and Diego Moussallem and Gustavo Publio",
title = "Neural Machine Translation for Query Construction and Composition",
year = "2018",
journal = "ICML Workshop on Neural Abstract Machines \& Program Induction (NAMPI v2)",
url = "https://arxiv.org/abs/1806.10478",
}
@inproceedings{panchbhai-2020,
author = "Anand Panchbhai and Tommaso Soru and Edgard Marx",
title = "Exploring Sequence-to-Sequence Models for {SPARQL} Pattern Composition",
year = "2020",
journal = "First Indo-American Knowledge Graph and Semantic Web Conference",
url = "https://arxiv.org/abs/2010.10900",
}
- Primary contacts: Tommaso Soru and Edgard Marx.
- Neural SPARQL Machines mailing list.
- Join the conversation on Gitter.
- Follow the project on ResearchGate.
- Follow Liber AI Research on Twitter.