Neural Databases.
In recent years, neural networks have shown impressive performance gains on
long-standing AI problems, and in particular, answering queries from natural
language text. These advances raise the question of whether they can be
extended to a point where we can relax the fundamental assumption of database
management, namely, that our data is represented as fields of a pre-defined
schema.
This paper presents a first step in answering that question. We describe
NeuralDB, a database system with no pre-defined schema, in which updates and
queries are given in natural language. We develop query processing techniques
that build on the primitives offered by the state of the art Natural Language
Processing methods.
We begin by demonstrating that at the core, recent NLP transformers, powered
by pre-trained language models, can answer select-project-join queries if they
are given the exact set of relevant facts. However, they cannot scale to
non-trivial databases and cannot perform aggregation queries. Based on these
findings, we describe a NeuralDB architecture that runs multiple Neural SPJ
operators in parallel, each with a set of database sentences that can produce
one of the answers to the query. The result of these operators is fed to an
aggregation operator if needed. We describe an algorithm that learns how to
create the appropriate sets of facts to be fed into each of the Neural SPJ
operators. Importantly, this algorithm can be trained by the Neural SPJ
operator itself. We experimentally validate the accuracy of NeuralDB and its
components, showing that we can answer queries over thousands of sentences with
very high accuracy.
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