Journal
PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14)
Volume -, Issue -, Pages 1126-1135Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2623330.2623617
Keywords
Information extraction; spreadsheets; graphical model
Categories
Funding
- National Science Foundation [IIS-1054913, IIS-1064606]
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1064606] Funding Source: National Science Foundation
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Spreadsheets contain valuable data on many topics. However, spreadsheets are difficult to integrate with other data sources. Converting spreadsheet data to the relational model would allow data analysts to use relational integration tools. We propose a two-phase semiautomatic system that extracts accurate relational metadata while minimizing user effort. Based on an undirected graphical model, our system enables downstream spreadsheet integration applications. First, the automatic extractor uses hints from spreadsheets' graphical style and recovered metadata to extract the spreadsheet data as accurately as possible. Second, the interactive repair identifies similar regions in distinct spreadsheets scattered across large spreadsheet corpora, allowing a user's single manual repair to be amortized over many possible extraction errors. Our experiments show that a human can obtain the accurate extraction with just 31% of the manual operations required by a standard classification based technique on two real-world datasets.
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