NDLI: Efficiently incorporating user feedback into information extraction and integration programs

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Efficiently incorporating user feedback into information extraction and integration programs

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Efficiently incorporating user feedback into information extraction and integration programs

Content Provider	ACM Digital Library
Author	Chai, Xiaoyong Doan, AnHai Naughton, Jeffrey F. Vuong, Ba-Quy
Abstract	Many applications increasingly employ information extraction and integration (IE/II) programs to infer structures from unstructured data. Automatic IE/II are inherently imprecise. Hence such programs often make many IE/II mistakes, and thus can significantly benefit from user feedback. Today, however, there is no good way to automatically provide and process such feedback. When finding an IE/II mistake, users often must alert the developer team (e.g., via email or Web form) about the mistake, and then wait for the team to manually examine the program internals to locate and fix the mistake, a slow, error-prone, and frustrating process. In this paper we propose a solution for users to directly provide feedback and for IE/II programs to automatically process such feedback. In our solution a developer U uses hlog, a declarative IE/II language, to write an IE/II program P. Next, U writes declarative user feedback rules that specify which parts of P's data (e.g., input, intermediate, or output data) users can edit, and via which user interfaces. Next, the so-augmented program P is executed, then enters a loop of waiting for and incorporating user feedback. Given user feedback F on a data portion of P, we show how to automatically propagate F to the rest of P, and to seamlessly combine F with prior user feedback. We describe the syntax and semantics of hlog, a baseline execution strategy, and then various optimization techniques. Finally, we describe experiments with real-world data that demonstrate the promise of our solution.
Starting Page	87
Ending Page	100
Page Count	14
File Format	PDF
ISBN	9781605585512
DOI	10.1145/1559845.1559857
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-06-29
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Information integration Provenance Incremental execution Information extraction User feedback
Content Type	Text
Resource Type	Article

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