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A Prototype for Semantic Querying and Evaluation of Textual Data

This undergraduate research project addresses the problem of semantic-aware search in textual SQL databases. This problem has emerged as a required extension of the standard containment keyword-based query to meet user needs in textual databases and IR applications. We build on top of a semantic-aware inverted index called SemIndex, previously developed by our peers, to allow semantic-aware search, result selection, and result ranking functionality. Various weighting functions and search algorithms have been developed for that purpose. An easy to use graphical interface was also designed to help testers write and execute their queries. Preliminary experiments reflect the effectiveness and efficiency of our solution. Report in pdf


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