Sciweavers

Share
SIGMOD
2011
ACM

Hybrid in-database inference for declarative information extraction

7 years 9 months ago
Hybrid in-database inference for declarative information extraction
In 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 ...
Daisy Zhe Wang, Michael J. Franklin, Minos N. Garo
Added 17 Sep 2011
Updated 17 Sep 2011
Type Journal
Year 2011
Where SIGMOD
Authors Daisy Zhe Wang, Michael J. Franklin, Minos N. Garofalakis, Joseph M. Hellerstein, Michael L. Wick
Comments (0)
books