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

AbstractMany 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