Hybrid in-database inference for declarative information extraction
(EN)
Γαροφαλακης Μινως
(EL)
Hellerstein Joseph M.
(EN)
Garofalakis Minos
(EN)
Wick Michael L.
(EN)
Wang Daisy Zhe
(EN)
Franklin Michael J.
(EN)
Πολυτεχνείο Κρήτης
(EL)
Technical University of Crete
(EN)
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 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.
(EN)
