Mining frequent patterns by pattern-growth: methodology and implications
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
WebTables: exploring the power of tables on the web
Proceedings of the VLDB Endowment
Extracting data records from the web using tag path clustering
Proceedings of the 18th international conference on World wide web
TextRunner: open information extraction on the web
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Data integration for the relational web
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Building enriched web page representations using link paths
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The parallel path framework for entity discovery on the web
ACM Transactions on the Web (TWEB)
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The World-Wide Web consists not only of a huge number of unstructured texts, but also a vast amount of valuable structured data. Web tables [2] are a typical type of structured information that are pervasive on the web, and Web-scale methods that automatically extract web tables have been studied extensively [1]. Many powerful systems (e.g.OCTOPUS [4], Mesa [3]) use extracted web tables as a fundamental component. In the database vernacular, a table is defined as a set of tuples which have the same attributes. Similarly, a web table is defined as a set of rows (corresponding to database tuples) which have the same column headers (corresponding to database attributes). Therefore, to extract a web table is to extract a relation on the web. In databases, tables often contain foreign keys which refer to other tables. Therefore, it follows that hyperlinks inside a web table sometimes function as foreign keys to other relations whose tuples are contained in the hyperlink's target pages. In this paper, we explore this idea by asking: can we discover new attributes for web tables by exploring hyperlinks inside web tables? This poster proposes a solution that takes a web table as input. Frequent patterns are generated as new candidate relations by following hyperlinks in the web table. The confidence of candidates are evaluated, and trustworthy candidates are selected to become new attributes for the table. Finally, we show the usefulness of our method by performing experiments on a variety of web domains.