Uncovering the relational web

Uncovering the relational web (2008)

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by Michael J. Cafarella , Eugene Wu

Venue:	In under review
Citations:	14 - 6 self

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Datum	Value	Source
TITLE	Uncovering the relational web	INFERENCE
AUTHOR NAME	Michael J. Cafarella	SVM HeaderParse 0.2
AUTHOR AFFIL	University of Washington	SVM HeaderParse 0.2
AUTHOR NAME	Eugene Wu	SVM HeaderParse 0.2
AUTHOR AFFIL	MIT	SVM HeaderParse 0.2
ABSTRACT	The World-Wide Web consists of a huge number of unstructured hypertext documents, but it also contains structured data in the form of HTML tables. Many of these tables contain both relational-style data and a small “schema ” of labeled and typed columns, making each such table a small structured database. The WebTables project is an effort to extract and make use of the huge number of these structured tables on the Web. A clean collection of relational-style tables could be useful for improving web search, schema design, and many other applications. This paper describes the first stage of the WebTables project. First, we give an in-depth study of the Web’s HTML table corpus. For example, we extracted 14.1 billion HTML tables from a several-billion-page portion of Google’s generalpurpose web crawl, and estimate that 154 million of these tables contain high-quality relational-style data. We also describe the crawl’s distribution of table sizes and data types. Second, we describe a system for performing relation recovery. The Web mixes relational and non-relational tables indiscriminately (often on the same page), so there is no simple way to distinguish the 1.1 % of good relations from the remainder, nor to recover column label and type information. Our mix of hand-written detectors and statistical classifiers takes a raw Web crawl as input, and generates a collection of databases that is five orders of magnitude larger than any other collection we are aware of. Relation recovery achieves precision and recall that are comparable to other domain-independent information extraction systems. 1.	SVM HeaderParse 0.2
YEAR	2008	INFERENCE
VENUE	In under review	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	15 found	ParsCit 1.0