Natural Language Query (NLQ) is a subset of Natural Language Processing (NLP), which allows users to interact with systems using everyday, conversational language. The main goal of NLQ is to enable users to ask questions to databases or other information repositories in a natural, human-like manner, without the need to learn complex query languages such as SQL. This makes sophisticated, data-driven analysis accessible to a wider audience without requiring deep technical know-how.
The foundation of NLQ technology involves complex algorithms that comprehend human language inputs, detecting nuances like context, syntax, semantics, and even misused words. Once a user’s question is inputted, the NLQ system translates that question into a format that can be interpreted by the database. It then retrieves the relevant data and, often with the help of Natural Language Generation (NLG), translates that data back into a natural language answer that a user can understand.
NLQ applications are ubiquitous, encompassing areas such as business intelligence platforms, virtual assistants like Siri and Alexa, and customer service chatbots. In each case, they bridge the gap between human users and complex data operating systems. By making the retrieval of data as simple as asking a question, NLQ marks a fundamental shift in the way we interact with technology. Despite facing challenges in interpreting ambiguities in human language, the field has made significant advances and continues to improve through ongoing research and development.
