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Natural language interfaces provide an easy way to query and
interact with data and enable non-technical users to investigate
data sets without the need to know a query language. Recent
advances in natural language understanding and processing have
resulted in a renewed interest in natural language interfaces to
data. The main challenges in natural language querying are
identifying the entities involved in the user utterance, connecting
the different entities in a meaningful way over the underlying data
source to interpret user intents, and generating a structured
query. There are two main approaches in the literature for
interpreting a user's natural language query. The first are
rule-based systems that make use of semantic indices, ontologies,
and knowledge graphs to identify the entities in the query,
understand the intended relationships between those entities, and
utilize grammars to generate the target queries. Second are hybrid
approaches that utilize both rule-based techniques as well as deep
learning models. Conversational interfaces are the next natural
step to one-shot natural language querying by exploiting query
context between multiple turns of conversation for disambiguation.
In this monograph, the authors review the rule-based and hybrid
technologies that are used in natural language interfaces and
survey the different approaches to natural language querying. They
also describe conversational interfaces for data analytics and
discuss several benchmarks used for natural language querying
research and evaluation. The monograph concludes with discussion on
challenges that need to be addressed before these systems can be
widely adopted.
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