Abstract
Accessing information is a vital activity in businesses; therefore, databases (DBs) have become necessary tools for storing their information. However, for accessing the information stored in a database, it is necessary to use a DB query language, such as SQL. Natural language interfaces to databases (NLIDBs) allow inexperienced users to obtain information from a DB using natural language expressions without the need of using a DB query language. Despite the relative effectiveness of NLIDBs, most of the approaches proposed for designing NLIDBs ignore the possibility that the DB to be queried could be poorly designed; i.e., it could have design anomalies. Unfortunately, various experiments (described in this paper) show that DB anomalies degrade the performance (recall) of NLIDBs. The purpose of this paper is to analyze the most common DB design anomalies for proposing solutions to this problem and avoid performance degradation of NLIDBs when accessing such DBs.
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Pazos R., R.A., Martínez F., J.A., Aguirre L., A.G., Aguirre L., M.A. (2018). Issues in Querying Databases with Design Anomalies Using Natural Language Interfaces. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications. Studies in Computational Intelligence, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-319-71008-2_33
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DOI: https://doi.org/10.1007/978-3-319-71008-2_33
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