Abstract
The top-k ranking is based on some scoring function in deterministic applications. However, in uncertain applications, such a clean definition does not exist, since the process of reporting a tuple in a top-k answer does not depend only on its score but also on its membership probability. This work introduces an approach to processing top-k queries based on statistical information extraction model, which enables us to determine the probability that an observed extraction is correct. We validate the performance of the model empirically on the task of extracting information from two data sets.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Advancements in Computing Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
