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Hybrid in-database inference for declarative information extraction

Wang Daisy Zhe, Franklin Michael J., Garofalakis Minos, Hellerstein, Joseph, 1952-, Wick Michael L.

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URIhttp://purl.tuc.gr/dl/dias/9FBD290C-DC9B-419A-9A80-E532CB9521E0-
Identifierhttp://dl.acm.org/citation.cfm?id=1989323.1989378-
Identifierhttps://doi.org/10.1145/1989323.1989378-
Languageen-
Extent12 pagesen
TitleHybrid in-database inference for declarative information extractionen
CreatorWang Daisy Zheen
CreatorFranklin Michael J.en
CreatorGarofalakis Minosen
CreatorΓαροφαλακης Μινωςel
CreatorHellerstein, Joseph, 1952-en
CreatorWick Michael L.en
PublisherAssociation for Computing Machineryen
Content SummaryIn 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
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-11-30-
Date of Publication2011-
SubjectDatabase managementen
SubjectMathematics of computingen
Bibliographic CitationD. Z. Wang, M. J. Franklin, M. Garofalakis, J. M. Hellerstein and M. L. Wick, "Hybrid in-database inference for declarative information extraction", in ACM SIGMOD International Conference on Management of Data, 2011, pp. 517-528. doi: 10.1145/1989323.1989378 en

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