Abstract:
Keyword search is an easy and potentially
effective way to find information that is stored in
relational database for ordinary users or web
users. As results needed by user are assembled
from joining tuples of multiple relations, ranking
keyword queries are needed to retrieve relevant
results by a given keyword query. For a given
keyword query, we first generate a set of joining
tuples, such as candidate networks (CNs). We
then model the generated CN as a document. We
evaluate the score for each document to estimate
its relevance to a given keyword query. Finally,
we rank the relevant queries by using each
evaluated score as high as possible. In this
paper, we propose a new ranking method by
adapting existing IR scoring techniques based on
the virtual document. We evaluate the proposed
ranking method on DBLP dataset. The
experimental results are shown by comparison of
the proposed ranking method and the previous
IR ranking method.
