@inproceedings{651e5ec62307469b9244140829c8fb14,
title = "Exploiting correlation to rank database query results",
abstract = "In recent years, effective ranking strategies for relational databases have been extensively studied. Existing approaches have adopted empirical term-weighting strategies called tf×idf (term frequency times inverse document frequency) schemes from the field of information retrieval (IR) without careful consideration of relational model. This paper proposes a novel ranking scheme that exploits the statistical correlations, which represent the underlying semantics of the relational model. We extend Bayesian network models to provide dependence structure in relational databases. Furthermore, a limited assumption of value independence is defined to relax the unrealistic execution cost of the probabilistic model. Experimental results show that our model is competitive in terms of efficiency without losing the quality of query results.",
keywords = "Attribute value, Bayesian networks, Correlation, Keyword search over structured data, Ranking",
author = "Jaehui Park and Lee, {Sang Goo}",
year = "2011",
doi = "10.1007/978-3-642-20152-3_22",
language = "English",
isbn = "9783642201516",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 2",
pages = "296--305",
booktitle = "Database Systems for Advanced Applications - 16th International Conference, DASFAA 2011, Proceedings",
edition = "PART 2",
note = "16th International Conference on Database Systems for Advanced Applications, DASFAA 2011 ; Conference date: 22-04-2011 Through 25-04-2011",
}