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Although there have been efforts to learn extractors from automatically generated labels, these methods are not sufficiently robust to succeed in settings with complex schemas and information-rich websites.<\/jats:p>\n          <jats:p>In this paper we present a new method for automatic extraction from semi-structured websites based on distant supervision. We automatically generate training labels by aligning an existing knowledge base with a website and leveraging the unique structural characteristics of semi-structured websites. We then train a classifier based on the potentially noisy and incomplete labels to predict new relation instances. Our method can compete with annotation-based techniques in the literature in terms of extraction quality. A large-scale experiment on over 400,000 pages from dozens of multi-lingual long-tail websites harvested 1.25 million facts at a precision of 90%.<\/jats:p>","DOI":"10.14778\/3231751.3231758","type":"journal-article","created":{"date-parts":[[2018,7,27]],"date-time":"2018-07-27T12:21:07Z","timestamp":1532694067000},"page":"1084-1096","source":"Crossref","is-referenced-by-count":34,"title":["CERES"],"prefix":"10.14778","volume":"11","author":[{"given":"Colin","family":"Lockard","sequence":"first","affiliation":[{"name":"University of Washington"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin Luna","family":"Dong","sequence":"additional","affiliation":[{"name":"Amazon"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arash","family":"Einolghozati","sequence":"additional","affiliation":[{"name":"Facebook"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prashant","family":"Shiralkar","sequence":"additional","affiliation":[{"name":"Amazon"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,6]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"http:\/\/www.commoncrawl.com","year":"2017","unstructured":"Commoncrawl corpus. http:\/\/www.commoncrawl.com , 2017 . 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