Harvesting relational tables from lists on the web

Harvesting relational tables from lists on the web A large number of web pages contain data structured in the form of “lists”. Many such lists can be further split into multi-column tables, which can then be used in more semantically meaningful tasks. However, harvesting relational tables from such lists can be a challenging task. The lists are manually generated and hence need not have well-defined templates—they have inconsistent delimiters (if any) and often have missing information. We propose a novel technique for extracting tables from lists. The technique is domain independent and operates in a fully unsupervised manner. We first use multiple sources of information to split individual lines into multiple fields and then, compare the splits across multiple lines to identify and fix incorrect splits and bad alignments. In particular, we exploit a corpus of HTML tables, also extracted from the web, to identify likely fields and good alignments. For each extracted table, we compute an extraction score that reflects our confidence in the table’s quality. We conducted an extensive experimental study using both real web lists and lists derived from tables on the web. The experiments demonstrate the ability of our technique to extract tables with high accuracy. In addition, we applied our technique on a large sample of about 100,000 lists crawled from the web. The analysis of the extracted tables has led us to believe that there are likely to be tens of millions of useful and query-able relational tables extractable from lists on the web. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Harvesting relational tables from lists on the web

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Publisher
Springer-Verlag
Copyright
Copyright © 2011 by Springer-Verlag
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-011-0223-0
Publisher site
See Article on Publisher Site

Abstract

A large number of web pages contain data structured in the form of “lists”. Many such lists can be further split into multi-column tables, which can then be used in more semantically meaningful tasks. However, harvesting relational tables from such lists can be a challenging task. The lists are manually generated and hence need not have well-defined templates—they have inconsistent delimiters (if any) and often have missing information. We propose a novel technique for extracting tables from lists. The technique is domain independent and operates in a fully unsupervised manner. We first use multiple sources of information to split individual lines into multiple fields and then, compare the splits across multiple lines to identify and fix incorrect splits and bad alignments. In particular, we exploit a corpus of HTML tables, also extracted from the web, to identify likely fields and good alignments. For each extracted table, we compute an extraction score that reflects our confidence in the table’s quality. We conducted an extensive experimental study using both real web lists and lists derived from tables on the web. The experiments demonstrate the ability of our technique to extract tables with high accuracy. In addition, we applied our technique on a large sample of about 100,000 lists crawled from the web. The analysis of the extracted tables has led us to believe that there are likely to be tens of millions of useful and query-able relational tables extractable from lists on the web.

Journal

The VLDB JournalSpringer Journals

Published: Apr 1, 2011

References

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