Majdi Owda

This paper presents a novel methodology of incorporating Information Extraction (IE) techniques into an Enhanced Conversation-Based Interface to Relational Databases (C-BIRD) in order to generate dynamic SQL queries. Conversational Agents can converse with the user in natural language about a specific problem domain. In C-BIRD, such agents allow a user to converse with a relational database in order to retrieve answers to queries without knowledge of SQL. A Knowledge Tree is used to direct the Conversational Agent towards the goal i.e. creating an SQL query to fit the user’s natural language enquiry. The use of IE techniques such as template filling helps in answering the user’s queries by processing the user’s dialogue and extracts understandable patterns that fills the SQL templates. The developed prototype system increases the number of answered natural language queries in comparison to hardcoded decision paths in the knowledge trees.

This paper presents a novel methodology of incorporating Information Extraction (IE) techniques into an Enhanced Conversation-Based Interface to Relational Databases (C-BIRD) in order to generate dynamic SQL queries. Conversational Agents can converse with the user in natural language about a specific problem domain. In C-BIRD, such agents allow a user to converse with a relational database in order to retrieve answers to queries without knowledge of SQL.

Abstract 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 (ie specific domain).