Computing machinery and intelligence
Computation & intelligence
Developing a natural language interface to complex data
ACM Transactions on Database Systems (TODS)
ELIZA—a computer program for the study of natural language communication between man and machine
Communications of the ACM
Toward conversational human-computer interaction
AI Magazine
Towards a theory of natural language interfaces to databases
Proceedings of the 8th international conference on Intelligent user interfaces
MASQUE/SQL: an efficient and portable natural language query interface for relational databases
IEA/AIE'93 Proceedings of the 6th international conference on Industrial and engineering applications of artificial intelligence and expert systems
Journal of the American Society for Information Science and Technology
Conversation-Based Natural Language Interface to Relational Databases
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
C-Phrase: A system for building robust natural language interfaces to databases
Data & Knowledge Engineering
AMRAPALIKA: An expert system for the diagnosis of pests, diseases, and disorders in Indian mango
Knowledge-Based Systems
Adaptive tutoring in an intelligent conversational agent system
Transactions on Computational Collective Intelligence VIII
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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.