SPARQL as a Foreign Language
Abstract
In the last years, the Linked Data Cloud has achieved a size of more than 100 billion facts pertaining to a multitude of domains. However, accessing this information has been significantly challenging for lay users. Approaches to problems such as Question Answering on Linked Data and Link Discovery have notably played a role in increasing information access. These approaches are often based on handcrafted and/or statistical models derived from data observation. Recently, Deep Learning architectures based on Neural Networks called seq2seq have shown to achieve state-of-the-art results at translating sequences into sequences. In this direction, we propose Neural SPARQL Machines, end-to-end deep architectures to translate any natural language expression into sentences encoding SPARQL queries. Our preliminary results, restricted on selected DBpedia classes, show that Neural SPARQL Machines are a promising approach for Question Answering on Linked Data, as they can deal with known problems such as vocabulary mismatch and perform graph pattern composition.
Cite
@article{arxiv.1708.07624,
title = {SPARQL as a Foreign Language},
author = {Tommaso Soru and Edgard Marx and Diego Moussallem and Gustavo Publio and André Valdestilhas and Diego Esteves and Ciro Baron Neto},
journal= {arXiv preprint arXiv:1708.07624},
year = {2020}
}
Comments
SEMANTiCS 2017; 13th International Conference on Semantic Systems, 2017