Neural SPARQL Machines

A booming amount of information is continuously added to the Internet as structured and unstructured data, feeding knowledge bases such as DBpedia and Wikidata with billions of statements describing millions of entities. The aim of Question Answering systems is to allow lay users to access such data using natural language without needing to write formal queries. However, users often submit questions that are complex and require a certain level of abstraction and reasoning to decompose them into basic graph patterns. In this short paper, we explore the use of architectures based on Neural Machine Translation called Neural SPARQL Machines to learn pattern compositions. We show that sequence-to-sequence models are a viable and promising option to transform long utterances into complex SPARQL queries.

The Semantic Web community has successfully deployed a significant number of RDF datasets on the Web. However, for ordinary users, to access information available on this datasets, it is still a cumbersome and time-demanding task that requires the knowledge of description frameworks the use of formal languages. We introduce the Neural SPARQL Machine (NSpM) Telegram Chatbot, a chatbot built on top the open-source Telegram platform that facilitates the natural language querying of RDF datasets through the NSpM architecture. We show the different APIs implemented by the proposed chatbot as well as the processing steps from a natural language query to its corresponding answer. Additionally, we demonstrate how to instantiate as well as how to query other RDF datasets.

Research on question answering with knowledge base has recently seen an increasing use of deep architectures. In this extended abstract, we study the application of the neural machine translation paradigm for question parsing. We employ a sequence-to-sequence model to learn graph patterns in the SPARQL graph query language and their compositions. Instead of inducing the programs through question-answer pairs, we expect a semi-supervised approach, where alignments between questions and queries are built through templates. We argue that the coverage of language utterances can be expanded using late notable works in natural language generation.

The role of Question Answering is central to the fulfillment of the Semantic Web. Recently, several approaches relying on artificial neural networks have been proposed to tackle the problem of question answering over knowledge graphs. Such techniques are however known to be data-hungry and the creation of training sets requires a substantial manual effort. We thus introduce Dbnqa, a comprehensive dataset of 894,499 pairs of questions and SPARQL queries based on templates which are specifically designed on the DBpedia knowledge base. We show how the method used to generate our dataset can be easily reused for other purposes. We report the successful adoption of Dbnqa in an experimental phase and present how it compares with existing question-answering corpora.

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.