OREGON STATE UNIVERSITY

You are here

Predicting the effectiveness of keyword queries on databases

TitlePredicting the effectiveness of keyword queries on databases
Publication TypeConference Paper
Year of Publication2012
AuthorsCheng, S., A. Termehchy, and V. Hristidis
Conference Name21st ACM International Conference on Information and Knowledge Management (CIKM 2012)
Pagination1213-1222
Date Published11/2012
PublisherACM Press
Conference LocationMaui, Hawaii
ISBN Number9781450311564
Abstract

Keyword query interfaces (KQIs) for databases provide easy access to data, but often suffer from low ranking quality, i.e. low precision and/or recall, as shown in recent benchmarks. It would be useful to be able to identify queries that are likely to have low ranking quality to improve the user satisfaction. For instance, the system may suggest to the user alternative queries for such hard queries. In this paper, we analyze the characteristics of hard queries and propose a novel framework to measure the degree of difficulty for a keyword query over a database, considering both the structure and the content of the database and the query results. We evaluate our query difficulty prediction model against two relevance judgment benchmarks for keyword search on databases, INEX and SemSearch. Our study shows that our model predicts the hard queries with high accuracy. Further, our prediction algorithms incur minimal time overhead.

DOI10.1145/2396761.2398422