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- W1128683088 abstract "Matching and merging data from conflicting sources is the bread and butter of data integration, which drives search verticals, e-commerce comparison sites and cyber intelligence. Schema matching lifts data integration - traditionally focused on well-structured data - to highly heterogeneous sources. While schema matching has enjoyed significant success in matching data attributes, inconsistencies can exist at a deeper level, making full integration difficult or impossible. We propose a more fine-grained approach that focuses on correspondences between the values of attributes across data sources. Since the semantics of attribute values derive from their use and co-occurrence, we argue for the suitability of canonical correlation analysis (CCA) and its variants. We demonstrate the superior statistical and computational performance of multiple sparse CCA compared to a suite of baseline algorithms, on two datasets which we are releasing to stimulate further research. Our crowd-annotated data covers both cases that are relatively easy for humans to supply ground-truth, and that are inherently difficult for human computation." @default.
- W1128683088 created "2016-06-24" @default.
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- W1128683088 date "2015-02-18" @default.
- W1128683088 modified "2023-10-18" @default.
- W1128683088 title "Sub-Merge: Diving Down to the Attribute-Value Level in Statistical Schema Matching" @default.
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- W1128683088 doi "https://doi.org/10.1609/aaai.v29i1.9459" @default.
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