Paper Info
Title
Language models for keyword search over data graphs
Abstract
In keyword search over data graphs, an answer is a non-redundant 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.
Year
DOI
Venue
2012
10.1145/2124295.2124340
WSDM
Keywords
Field
DocType
data graph,existing system,novel approach,existing algorithm returns candidate,keyword search,language model,basic knowledge,keyword query,candidate answer,language models,ranking
Graph,Data mining,Information retrieval,Ranking,Computer science,Keyword search,Tree (data structure),Robustness (computer science),Natural language processing,Artificial intelligence,Language model
Conference
Citations 
PageRank 
References 
7
0.47
19
Authors
2
Name
Order
Citations
PageRank
Yosi Mass157460.91
Yehoshua Sagiv253621575.95