کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4945337 | 1438421 | 2017 | 24 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
ERBlox: Combining matching dependencies with machine learning for entity resolution
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
Entity resolution (ER), an important and common data cleaning problem, is about detecting data duplicate representations for the same external entities, and merging them into single representations. Relatively recently, declarative rules called matching dependencies (MDs) have been proposed for specifying similarity conditions under which attribute values in database records are merged. In this work we show the process and the benefits of integrating four components of ER: (a) Building a classifier for duplicate/non-duplicate record pairs built using machine learning (ML) techniques; (b) Use of MDs for supporting the blocking phase of ML; (c) Record merging on the basis of the classifier results; and (d) The use of the declarative language LogiQL-an extended form of Datalog supported by the LogicBlox platform-for all activities related to data processing, and the specification and enforcement of MDs.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Approximate Reasoning - Volume 83, April 2017, Pages 118-141
Journal: International Journal of Approximate Reasoning - Volume 83, April 2017, Pages 118-141
نویسندگان
Zeinab Bahmani, Leopoldo Bertossi, Nikolaos Vasiloglou,