Abstract: Methods and systems are disclosed for bridging a natural language processing (NLP) engine to a database engine, enabling natural language queries to be handled by the database engine. The NLP engine produces a query structure, containing an intent and one or more query parameters, for a natural language query. Rules are successively applied to the query structure. When a rule matches, a corresponding action is performed to identify a sub-intent, transform query parameters, or generate a filter string. The sub-intent is mapped to a query destination, which can be an intrinsic or extrinsic service offered by the data engine. A query string is constructed, including the query destination and any filter strings, and transmitted to the database engine to answer the original query. The disclosed technologies are flexible, easily reconfigurable, and future-proof. Variations and use cases are disclosed.

Abstract: Methods and systems are disclosed for bridging a natural language processing (NLP) engine to a database engine, enabling natural language queries to be handled by the database engine. The NLP engine produces a query structure, containing an intent and one or more query parameters, for a natural language query. Rules are successively applied to the query structure. When a rule matches, a corresponding action is performed to identify a sub-intent, transform query parameters, or generate a filter string. The sub-intent is mapped to a query destination, which can be an intrinsic or extrinsic service offered by the data engine. A query string is constructed, including the query destination and any filter strings, and transmitted to the database engine to answer the original query. The disclosed technologies are flexible, easily reconfigurable, and future-proof. Variations and use cases are disclosed.