Year: 2014
Type: conference-paper
Abstract: Managers of companies are typically not SQL (Structured Query Language) experts but require information 24/7. Therefore, a growing need for Natural Language Interfaces to Databases (NLIDs) has been identified, with a vast amount of research being undertaken in the area. The existing approaches to NLIDs present many weaknesses including the inability to deal with grammatical mistakes in user input, the inability to communicate with the user to correct mistakes and the inability to allow refinement of query results. This paper proposes a system, SEEKER, which uses a Conversational Agent (CA) as the Natural Language Interface (NLI) in a NLID. The CA is used to capture key words in the user's utterance. Once these key words have been identified, the most appropriate SQL template is selected by the expert system using rule based reasoning. The identified variables are mapped to the SQL template in order to create an SQL query. SEEKER allows for refinement of query results. SEEKER was evaluated in terms of user satisfaction and task completion. The results of the evaluation were promising.
Authors: Emma V. Smith, Keeley Crockett, Annabel Latham, Fiona Buckingham
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2183574112