About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
WSDM 2012
Conference paper
Language models for keyword search over data graphs
Abstract
In keyword search over data graphs, an answer is a nonredundant subtree that includes the given keywords. This paper focuses on improving the effectiveness of that type of search. A novel approach that combines language models with structural relevance is described. The proposed approach consists of three steps. First, language models are used to assign dynamic, query-dependent weights to the graph. Those weights complement static weights that are pre-assigned to the graph. Second, an existing algorithm returns candidate answers based on their weights. Third, the candidate answers are re-ranked by creating a language model for each one. The effectiveness of the proposed approach is verified on a benchmark of three datasets: IMDB, Wikipedia and Mondial. The proposed approach outperforms all existing systems on the three datasets, which is a testament to its robustness. It is also shown that the effectiveness can be further improved by augmenting keyword queries with very basic knowledge about the structure. Copyright 2012 ACM.