NDLI: Self-supervised web search for any-k complete tuples

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Self-supervised web search for any-k complete tuples

Content Provider	ACM Digital Library
Author	Löser, Alexander Pieper, Stephan Nagel, Christoph Boden, Christoph
Abstract	A common task of Web users is querying structured information from Web pages. In this paper we propose a novel query processor for systematically discovering any-k relations from Web search results with conjunctive queries. The 'any-k' phrase denotes that retrieved tuples are not ranked by the system. For realizing this interesting scenario the query processor transfers a structured query into keyword queries that are submitted to a search engine, forwards search results to relation extractors, and then combines relations into result tuples. Unfortunately, relation extractors may fail to return a relation for a result tuple. We propose a solid information theory-based approach for retrieving missing attribute values of partially retrieved relations. Moreover, user-defined data sources may not return at least k complete result tuples. To solve this problem, we extend the Eddy query processing mechanism [14] for our 'querying the Web' scenario with a continuous, adaptive routing model. The model determines the most promising next incomplete row for returning any-k complete result tuples at any point during the query execution process. We report a thorough experimental evaluation over multiple relation extractors. Our experiments demonstrate that our query processor returns complete result tuples while processing only very few Web pages.
Starting Page	4
Ending Page	11
Page Count	8
File Format	PDF
ISBN	9781450306102
DOI	10.1145/1966883.1966889
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-03-25
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Text analytics Join execution on web search results
Content Type	Text
Resource Type	Article

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