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This paper describes an upgraded version of the SemIndex prototype system for semantic-aware search in textual SQL databases. Semantic-aware querying has emerged as a required extension of the standard containment keyword-based query to meet user needs in textual databases and IR applications. Here, we build on top of SemIndex, a semantic-aware inverted index previously developed by our team, to allow semantic-aware search, result selection, and result ranking functionality. Various weighting functions and intelligent search algorithms have been developed for that purpose and will be presented here. A graphical interface was also added to help end-users write and execute queries. Preliminary experiments highlight SemIndex querying effectiveness and efficiency, considering different querying algorithms, different semantic coverages, and a varying number of query keywords.
database indexing, deductive databases, graphical user interfaces, query processing, SQL

J. Tekli et al., "Upgraded SemIndex Prototype Supporting Intelligent Database Keyword Queries through Disambiguation, Query as You Type, and Parallel Search Algorithms," 2018 IEEE International Conference on Cognitive Computing (ICCC), San Francisco, CA, USA, 2018, pp. 33-40.
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