Record-boundary discovery in Web documents
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IEPAD: information extraction based on pattern discovery
Proceedings of the 10th international conference on World Wide Web
Automatic segmentation of text into structured records
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
A flexible learning system for wrapping tables and lists in HTML documents
Proceedings of the 11th international conference on World Wide Web
RoadRunner: Towards Automatic Data Extraction from Large Web Sites
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Web data extraction based on partial tree alignment
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WebTables: exploring the power of tables on the web
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EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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Morpheus: a deep web question answering system
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Harvesting relational tables from lists on the web
The VLDB Journal — The International Journal on Very Large Data Bases
Recovering semantics of tables on the web
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Information Processing and Management: an International Journal
An analysis of structured data on the web
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SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
LIEGE:: link entities in web lists with knowledge base
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Understanding tables on the web
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Towards web-scale structured web data extraction
Proceedings of the sixth ACM international conference on Web search and data mining
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ACM Transactions on the Web (TWEB)
A human-machine method for web table understanding
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Extraction and integration of partially overlapping web sources
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Scalable column concept determination for web tables using large knowledge bases
Proceedings of the VLDB Endowment
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IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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 have 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.