Semantic enrichment of database columns using generative language models for advanced query searches
Date of Award
2024
Publisher
North Dakota State University
Document Type
Master's Paper
Degree Awarded
Master of Science (MS)
Department
Computer Science
College
Engineering
Faculty Advisor
Straub, Jeremy
Description
This study introduces a novel application of natural language generation (NLG) models to improve database table retrieval. Unlike previous works primarily utilizing embeddings and natural language processing (NLP) models, this work explores using NLGs to generate database column descriptions to enhance search accuracy. The evaluation involves two main aspects: firstly, assessing the accuracy of AI-generated column descriptions compared to ground truth descriptions; secondly, examining the impact of these descriptions when integrated into existing search models to evaluate accuracy improvements. Results indicate improved semantic alignment when comparing generated descriptions to ground truth over column names alone and improved scores for established work.
Recommended Citation
Miska, Dylan, "Semantic enrichment of database columns using generative language models for advanced query searches" (2024). Computer Science. 370.
https://digitalcommons.ndsu.edu/computer-sci/370
Rights
NDSU policy 190.6.2
Rights Link
https://www.ndsu.edu/fileadmin/policy/190.pdf
Handle Identifier
https://hdl.handle.net/10365/33899