2,439 research outputs found
Natural Language Interfaces to Data
Recent advances in NLU and NLP have resulted in renewed interest in natural
language interfaces to data, which provide an easy mechanism for non-technical
users to access and query the data. While early systems evolved from keyword
search and focused on simple factual queries, the complexity of both the input
sentences as well as the generated SQL queries has evolved over time. More
recently, there has also been a lot of focus on using conversational interfaces
for data analytics, empowering a line of non-technical users with quick
insights into the data. There are three main challenges in natural language
querying (NLQ): (1) identifying the entities involved in the user utterance,
(2) connecting the different entities in a meaningful way over the underlying
data source to interpret user intents, and (3) generating a structured query in
the form of SQL or SPARQL.
There are two main approaches for interpreting a user's NLQ. Rule-based
systems make use of semantic indices, ontologies, and KGs to identify the
entities in the query, understand the intended relationships between those
entities, and utilize grammars to generate the target queries. With the
advances in deep learning (DL)-based language models, there have been many
text-to-SQL approaches that try to interpret the query holistically using DL
models. Hybrid approaches that utilize both rule-based techniques as well as DL
models are also emerging by combining the strengths of both approaches.
Conversational interfaces are the next natural step to one-shot NLQ by
exploiting query context between multiple turns of conversation for
disambiguation. In this article, we review the background technologies that are
used in natural language interfaces, and survey the different approaches to
NLQ. We also describe conversational interfaces for data analytics and discuss
several benchmarks used for NLQ research and evaluation.Comment: The full version of this manuscript, as published by Foundations and
Trends in Databases, is available at http://dx.doi.org/10.1561/190000007
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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
Automated Question-Answering for Interactive Decision Support in Operations & Maintenance of Wind Turbines
Intelligent question-answering (QA) systems have witnessed increased interest in recent years, particularly in their ability to facilitate information access, data interpretation or decision support. The wind energy sector is one of the most promising sources of renewable energy, yet turbines regularly suffer from failures and operational inconsistencies, leading to downtimes and significant maintenance costs. Addressing these issues requires rapid interpretation of complex and dynamic data patterns under time-critical conditions. In this article, we present a novel approach that leverages interactive, natural language-based decision support for operations & maintenance (O&M) of wind turbines. The proposed interactive QA system allows engineers to pose domain-specific questions in natural language, and provides answers (in natural language) based on the automated retrieval of information on turbine sub-components, their properties and interactions, from a bespoke domain-specific knowledge graph. As data for specific faults is often sparse, we propose the use of paraphrase generation as a way to augment the existing dataset. Our QA system leverages encoder-decoder models to generate Cypher queries to obtain domain-specific facts from the KG database in response to user-posed natural language questions. Experiments with an attention-based sequence-to-sequence (Seq2Seq) model and a transformer show that the transformer accurately predicts up to 89.75% of responses to input questions, outperforming the Seq2Seq model marginally by 0.76%, though being 9.46 times more computationally efficient. The proposed QA system can help support engineers and technicians during O&M to reduce turbine downtime and operational costs, thus improving the reliability of wind energy as a source of renewable energy
A review of NLIDB with deep learning: findings, challenges and open issues
Relational databases are storage for a massive amount of data. Knowledge of structured query language is a prior requirement to access that data. That is not possible for all non-technical personals, leading to the need for a system that translates text to SQL query itself rather than the user. Text to SQL task is also crucial because of its economic and industrial value. Natural Language Interface to Database (NLIDB) is the system that supports the text-to-SQL task. Developing the NLIDB system is a long-standing problem. Previously they were built based on domain-specific ontologies via pipelining methods. Recently a rising variety of Deep learning ideas and techniques brought this area to the attention again. Now end to end Deep learning models is being proposed for the task. Some publicly available datasets are being used for experimentation of the contributions, making the comparison process convenient. In this paper, we review the current work, summarize the research trends, and highlight challenging issues of NLIDB with Deep learning models. We discussed the importance of datasets, prediction model approaches and open challenges. In addition, methods and techniques are also summarized, along with their influence on the overall structure and performance of NLIDB systems. This paper can help future researchers start having prior knowledge of findings and challenges in NLIDB with Deep learning approaches
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