Hybrid in-database inference for declarative information extractionHybrid in-database inference for declarative information extraction Πλήρης Δημοσίευση σε Συνέδριο Conference Full Paper 2015-11-302011enIn the database community, work on information extraction (IE) has centered on two themes: how to effectively manage IE tasks, and how to manage the uncertainties that arise in the IE process in a scalable manner. Recent work has proposed a probabilistic database (PDB) based declarative IE system that supports a leading statistical IE model, and an associated inference algorithm to answer top-k-style queries over the probabilistic IE outcome. Still, the broader problem of effectively supporting general probabilistic inference inside a PDB-based declarative IE system remains open. In this paper, we explore the in-database implementations of a wide variety of inference algorithms suited to IE, including two Markov chain Monte Carlo algorithms, Viterbi and sum-product algorithms. We describe the rules for choosing appropriate inference algorithms based on the model, the query and the text, considering the trade-off between accuracy and runtime. Based on these rules, we describe a hybrid approach to optimize the execution of a single probabilistic IE query to employ different inference algorithms appropriate for different records. We show that our techniques can achieve up to 10-fold speedups compared to the non-hybrid solutions proposed in the literature.http://creativecommons.org/licenses/by/4.0/517-528 ACM SIGMOD International Conference on Management of DataProceedings of the 2011 ACM SIGMOD International Conference on Management of Data Wang Daisy Zhe Franklin Michael J. Garofalakis Minos Γαροφαλακης Μινως Hellerstein, Joseph, 1952- Wick Michael L. Association for Computing Machinery Database management Mathematics of computing