7 research outputs found
A Detailed Study on Aggregation Methods used in Natural Language Interface to Databases (NLIDB)
Historically, databases have been the most crucial issue in the study of information systems, and they constitute an essential part of all information management systems. Since, it complicated due to restricting the number of potential users, particularly non-expert database users who must comprehend the database structure to submit such queries. Natural language interface (NLI), the simplest method to retrieve information, is one possibility for interacting with the database. The transformation of a natural language query into a Structured Query (SQL) in a database is known as a "Natural Language Interface to Database" (NLIDB). This study uses NLIDB to handle the works performed under various aggregations with aggregation functions, a grouping phrase, and a possessing clause. This study carefully examines the numerous systematic aggregation approaches utilized in the NLIDB. This review provides extensive information about the many methods, including query-based, pattern-based, general, keyword-based NLIDB, and grammar-based systems, to extract data for a dissertation from a generic module for use in such systems that support query execution utilizing aggregations
SpatialNLI: A Spatial Domain Natural Language Interface to Databases Using Spatial Comprehension
A natural language interface (NLI) to databases is an interface that
translates a natural language question to a structured query that is executable
by database management systems (DBMS). However, an NLI that is trained in the
general domain is hard to apply in the spatial domain due to the idiosyncrasy
and expressiveness of the spatial questions. Inspired by the machine
comprehension model, we propose a spatial comprehension model that is able to
recognize the meaning of spatial entities based on the semantics of the
context. The spatial semantics learned from the spatial comprehension model is
then injected to the natural language question to ease the burden of capturing
the spatial-specific semantics. With our spatial comprehension model and
information injection, our NLI for the spatial domain, named SpatialNLI, is
able to capture the semantic structure of the question and translate it to the
corresponding syntax of an executable query accurately. We also experimentally
ascertain that SpatialNLI outperforms state-of-the-art methods.Comment: 10 page
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Using domain specific language and sequence to sequence models as a hybrid framework for a natural language interface to a database solution
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe aim of this project is to provide a new approach to solving the problem of
converting natural language into a language capable of querying a database or data
repository. This problem has been around for a while, in the 1970's the US Navy
developed a solution called LADDER and since then there have been an array of
solutions, approaches and tweaks that have kept the research community busy. The
introduction of electronic assistants into the smart phone in 2010 has given new
impetus to this problem.
With the increasingly pervasive nature of data and its ever expanding use to answer
questions within business science, medicine extracting data is becoming more important.
The idea behind this project is to make data more democratised by allowing access to it
without the need for specialist languages. The performance and reliability of converting
natural language into structured query language can be problematic in handling nuances
that are prevalent in natural language. Relational databases are not designed to understand
language nuance.
This project introduces the following components as part of a holistic approach to improving
the conversion of a natural language statement into a language capable of querying a data
repository.
● The idea proposed in this project combines the use of sequence to sequence models
in conjunction with the natural language part of speech technologies and domain
specific languages to convert natural language queries into SQL. The approach
being proposed by this chapter is to use natural language processing to perform an
initial shallow pass of the incoming query and then use Google's Tensor Flow to
refine the query with the use of a sequence to sequence model.
● This thesis is also proposing to use a Domain Specific Language (DSL) as part of the
conversion process. The use of the DSL has the potential to allow the natural
language query to be translated into more than just an SQL statement, but any query
language such as NoSQL or XQuery