Learning to construct knowledge bases from the World Wide Web

By Mark W. Craven, Dan DiPasquo, Dayne Freitag, Andrew McCallum, Tom Michael Mitchell, Kamal Nigam, Seán Slattery. 2000

560 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Information Retrieval & Search

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Citations in the corpus

Key-Value Memory Networks for Directly Reading Documents (2016, background): “Since information extraction (IE) (Craven et al. , 2000), intended to ﬁll in missing information in KBs, is not accurate nor reliable enough, collections of raw textual resources and documents, such as Wikipedia, will always contain more information.”
A Survey on Multi-view Learning (2013, methodology): “Since Blum and Mitchell (1998) first proposed the co-training algorithm and applied it to the web document classification problem, this novel method has caught the attention of many researchers and has been widely applied in the field of natural language processing (Craven et al., 2000; Müller et al”
Unsupervised named-entity extraction from the Web: An experimental study (2005, methodology): “K NOW I T A LL was inspired, in part, by the WebKB project [13].”
Learning from On-Line User Feedback in Neural Question Answering on the Web (2019, result): “This is in contrast to QA for knowledge bases, where a key objective involves building up the knowledge base itself [12, 16], as well as continuously updating the underlying decision rules for answering [2].”
Open Information Extraction from the Web (2007, methodology): “Automation The first step in automating IE was moving from knowledge-based IE systems to trainable systems that took as input hand-tagged instances [Riloff, 1996] or document segments [Craven et al., 1999] and automatically learned domain-specific extraction patterns.”
Mining data records in Web pages (2003, background): “Other approaches [2][4] [6][7][8][9][10][12][14][16][17][18][19][21][22][23] all have some degree of automation.”
Semi-Supervised Text Classification Using EM (2006, methodology): “With only labeled data, the naive Bayes classifier does a good job of classifying text documents (Lewis and Ringuette, 1994; Craven et al., 2000; Yang and Pedersen, 1997; Joachims, 1997; McCallum et al., 1998).”
Mining product reputations on the Web (2002, background): “[t is closely related to information extraction techniques (see e.g., [1], [4], [10], [ 8 ], [9], [7]), in which a wrapper or a specific extraction procedure is built automatically or marmally and used to extract specific pieces of information requested by a user.”
Adaptive computation and machine learning (1998, methodology): “With only labeled data, the naive Bayes classifier does a good job of classifying text documents (Lewis and Ringuette, 1994; Craven et al., 2000; Yang and Pedersen, 1997; Joachims, 1997; McCallum et al., 1998).”
Mining interesting knowledge from weblogs: a survey (2005, background): “The domain ontologies themselves may be pre-specified, or may be learned automatically from available training data [31].”
Link mining: a survey (2005, background): “Examples include predicting links among actors in social networks, such as predicting friendships; predicting the participation of actors in events [88], such as email, telephone calls and co-authorship; and predicting semantic relationships such as “advisor-of” based on web page links and content [”