Conversation-Based Natural Language Interface to Relational Databases

This paper proposes a new approach for creating conversation-based natural language interfaces to relational databases by combining goal oriented conversational agents and knowledge trees. Goal oriented conversational agents have proven their capability to disambiguate the user´s needs and to converse within a context (i.e. specific domain). Knowledge trees used to overcome the lacking of connectivity between the conversational agent and the relational database, through organizing the domain knowledge in knowledge trees. Knowledge trees also work as a road map for the conversational agent dialogue flow. The proposed framework makes it easier for knowledge engineers to develop a reliable conversation-based NLI-RDB. The developed prototype system shows excellent performance on common queries (i. e. queries extracted from expert by a knowledge engineer). The user will have a friendly interface that can converse with the relational database.