An approach for automatic query expansion based on NLP and semantics

Abstract

Nowadays, there is a huge amount of digital data stored in repositories that are queried by search systems that rely on keyword-based interfaces. Therefore, the retrieval of information from repositories has become an important issue. Organizations usually implement architectures based on relational databases that do not consider the syntax and semantics of the data. To solve this problem, they perform complex Extract, Transform and Load (ETL) processes from relational repositories to triple stores. However, most organizations do not carry out this migration due to lack of time, money and knowledge. In this paper we present a methodology that performs an automatic query expansion based on natural language processing and semantics to improve information retrieval from relational databases repositories. We have integrated it into an existing system in a real Media Group organization and we have tested it to analyze its effectiveness. Results obtained are promising and show the interest of the proposal. © 2014 Springer International Publishing Switzerland.