SPARQL as a Foreign Language
This work aims to improve information access for lay users by enabling natural language queries on linked data, representing an incremental advancement over existing methods.
The paper tackles the problem of accessing the large Linked Data Cloud by proposing Neural SPARQL Machines, which translate natural language into SPARQL queries, showing promising results for Question Answering on Linked Data by addressing issues like vocabulary mismatch and graph pattern composition.
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.