@inproceedings{7c761b5eb9d7464d8d55f2b85c97efa0,
title = "Probabilistic ranking for relational databases based on correlations",
abstract = "This paper proposes a ranking method to exploit statistical correlations among pairs of attribute values in relational databases. For a given query, the correlations of the query are aggregated with each of the attribute values in a tuple to estimate the relevance of that tuple to the query. We extend Bayesian network models to provide a probabilistic ranking function based on a limited assumption of value independence. Experimental results show that our model improves the retrieval effectiveness on real datasets and has a reasonable query processing time compared to related work.",
keywords = "Attribute value, Bayesian networks, Correlation, Keyword search over structured data, Ranking",
author = "Jaehui Park and Lee, {Sang Goo}",
year = "2010",
doi = "10.1145/1871902.1871917",
language = "English",
isbn = "9781450303859",
series = "International Conference on Information and Knowledge Management, Proceedings",
pages = "79--82",
booktitle = "Proceedings of the 3rd Workshop on Ph.D. Students in Information and Knowledge Management, PIKM'10, Co-located with 19th International Conference on Information and Knowledge Management, CIKM'10",
note = "3rd Workshop on Ph.D. Students in Information and Knowledge Management, PIKM'10, Co-located with 19th International Conference on Information and Knowledge Management, CIKM'10 ; Conference date: 26-10-2010 Through 30-10-2010",
}