P-top-k queries in a probabilistic framework from information extraction models

Many applications today need to manage uncertain data, such as information extraction (IE), data integration, sensor RFID networks, and scientific experiments. Top‐ $k$ queries are often natural and useful in analyzing uncertain data in those applications. In this paper, we study the problem of answering top‐ $k$ queries in a probabilistic framework from a state‐of‐the‐art statistical IE model–semi‐conditional random fields (CRFs)–in the setting of probabilistic databases that treat statistical models as first‐class data objects. We investigate the problem of ranking the answers to probabilistic database queries. We present an efficient algorithm for finding the best approximating parameters in such a framework for efficiently retrieving the top‐ $k$ ranked results. An empirical study using real data sets demonstrates the effectiveness of probabilistic top‐ $k$ queries and the efficiency of our method.