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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/9FBD290C-DC9B-419A-9A80-E532CB9521E0 | - |
| Identifier | http://dl.acm.org/citation.cfm?id=1989323.1989378 | - |
| Identifier | https://doi.org/10.1145/1989323.1989378 | - |
| Language | en | - |
| Extent | 12 pages | en |
| Title | Hybrid in-database inference for declarative information extraction | en |
| Creator | Wang Daisy Zhe | en |
| Creator | Franklin Michael J. | en |
| Creator | Garofalakis Minos | en |
| Creator | Γαροφαλακης Μινως | el |
| Creator | Hellerstein, Joseph, 1952- | en |
| Creator | Wick Michael L. | en |
| Publisher | Association for Computing Machinery | en |
| Content Summary | 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 |
| Type of Item | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Type of Item | Conference Full Paper | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2015-11-30 | - |
| Date of Publication | 2011 | - |
| Subject | Database management | en |
| Subject | Mathematics of computing | en |
| Bibliographic Citation | D. 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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