Pragmatic Approach to Structured Data Querying via Natural Language Interface
Introducing the research paper that describes a practical approach to building natural language interfaces for structured data querying.
As the use of technology increases and data analysis becomes integral in many businesses, the ability to quickly access and interpret data has become more important than ever.
Why build natural language interface for structured data querying?
Today’s information retrieval technologies utilized by companies claim to democratize data but the reality is that these technologies are very complex and require understanding of query languages, such as SQL, strong analytical skills, extensive training, and knowledge of data structure to formulate a valid query. Business people can barely use these systems without the help of a skilled business analyst.
Companies need to employ business analytics teams to help nondata professionals interact with enterprise data. These teams typically have an ever growing reporting backlog, as a result, even a simple question may take days to answer.
To reduce some burden on already overstretched data teams, many organizations are looking for self-service tools that allow non-developers to query databases using natural language without needing a data analyst for every report.
FriendlyData’s approach to structured data querying via natural language interface
At FriendlyData we are building a natural language interface for database querying. Our product translates natural language questions into corresponding SQL queries making data accessible to everyone in a company.
Last month we applied our query translation method to WikiSQL dataset (a large crowd-sourced dataset for developing natural language interfaces for relational databases).
FriendlyData’s query translation algorithm demonstrated high accuracy, in addition, it doesn’t require training on the massive datasets, which makes it easier and faster to implement compared to machine learning based algorithms.
Now we’re pleased to share the research paper “A pragmatic approach to structured data querying via natural language interface”, where we describe our algorithm in detail and discuss a number of factors that can dramatically affect the system architecture and the set of algorithms used to translate NL queries into a structured query representation.
Our primary goal is to help companies find the best solution when both high quality query translation and high security standards of architecture are required. Our method is designed for real-life business cases, where such factors as data security, time, scalability, and accuracy are mission critical. By no means will our approach be the best in every case, but our goal is to show what factors really matter for enterprises in real-life scenarios.
Find the whole paper on arxiv.org, and also be sure to follow us on Twitter, where we are sharing all the latest thinking in Natural Language Processing along with company news, papers, and other useful resources.
Democratize your data
FriendlyData helps to respond to one of the key challenges in the world of enterprise data — building the power of data and analytics into day-to-day decision-making.
The solution we offer is data democratization. FriendlyData makes data accessible to everyone by providing a user-friendly natural language search interface for databases.
Foster data-driven culture in your organization with us!
Originally published at www.friendlydata.io.
