Developing a natural language interface to complex data
ACM Transactions on Database Systems (TODS)
An English language question answering system for a large relational database
Communications of the ACM
Handbook of Natural Language Processing
Handbook of Natural Language Processing
NaLIX: an interactive natural language interface for querying XML
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
IEEE Internet Computing
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
Multilingual question answering with high portability on relational databases
MultiSumQA '02 proceedings of the 2002 conference on multilingual summarization and question answering - Volume 19
Generic querying of relational databases using natural language generation techniques
INLG '06 Proceedings of the Fourth International Natural Language Generation Conference
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Natural Language Interface to Database (NLIDB) systems have provided an easy access to database system without the need for the user to use formal query languages, such as SQL. Database query languages can be difficult to the non-expert users and learning these formal queries takes a lot of time. In NLIDB, users can type a question or a sentence in their natural language (such as English or Arabic) then it will be converted through a special natural language interface interrupter into formal database query. A major problem that faces the NLIDB designers is the identification of the tables that contain the required information and the desired attributes in the query. Most of the existing systems use static templates for the queries, in which tables' names are embedded in the template. However, this requires large code that considers all possible query templates. The main objective of this paper is to introduce a dynamic approach to determine the tables name that the attributes involved in the query belong to by representing the database schema as a graph which is used to determine the tables' names. This will minimize the templates used to build the queries thus minimizing the code size and effort to track and build all possible queries. We also propose a dynamic component that helps in building any NLIDB called database management. This component is linked to all the NLIDB system's components and will automatically build the dictionaries and database schema graph. The proposed approach was applied to a case study called Mall Shopper's Guide (MSG) to investigate its effectiveness.