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Statistical Entity Extraction From the Web
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DOI：10.1109/JPROC.2012.2191369.png)
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Abstract
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There are various kinds of valuable semantic information about real-world entities embedded in webpages and databases. Extracting and integrating these entity information from the Web is of great significance. Comparing to traditional information extraction problems, web entity extraction needs to solve several new challenges to fully take advantage of the unique characteristic of the Web. In this paper, we introduce our recent work on statistical extraction of structured entities, named entities, entity facts and relations from the Web. We also briefly introduce iKnoweb, an interactive knowledge mining framework for entity information integration. We will use two novel web applications, Microsoft Academic Search (aka Libra) and EntityCube, as working examples.
Keywords:
Crowdsourcing
entity extraction
entity relationship mining
entity search
interactive knowledge mining
named entity extraction
natural language processing
web page segmentation
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