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Learning to Extract Symbolic Knowledge from the World Wide Web

by: Mark Craven, Dan DiPasquo, Dayne Freitag, Andrew McCallum, Tom Mitchell, Kamal Nigam, and Seán Slattery

In: Proc. AAAI-98 (1998) .

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URL:	http://citeseer.nj.nec.com/9546.html

The World Wide Web is a vast source of information accessible to computers, but understandable only to humans. The goal of the research described here is to automatically create a computer understandable knowledge base whose content mirrors that of the World Wide Web. Such a knowledge base would enable much more effective retrieval of Web information, and promote new uses of the Web to support knowledge-based inference and problem solving. Our approach is to develop a trainable information extraction system that takes two inputs: and ontology defining the classes and relations of interest, and a set of training data consisting of labeled regions of hypertext representing instances of these classes and relations...

BibTeX record

@inproceedings{Craven:1998,
  abstract = {The World Wide Web is a vast source of information accessible to computers, but understandable only to humans. The goal of the research described here is to automatically create a computer understandable knowledge base whose content mirrors that of the World Wide Web. Such a knowledge base would enable much more effective retrieval of Web information, and promote new uses of the Web to support knowledge-based inference and problem solving. Our approach is to develop a trainable information extraction system that takes two inputs: and ontology defining the classes and relations of interest, and a set of training data consisting of labeled regions of hypertext representing instances of these classes and relations...},
  added-at = {2007-12-14T02:37:51.000+0100},
  author = {Craven, Mark and DiPasquo, Dan and Freitag, Dayne and McCallum, Andrew and Mitchell, Tom and Nigam, Kamal and Slattery, Se\'{a}n},
  biburl = {http://www.bibsonomy.org/bibtex/279604d45adda39d78379de3eb1af3f40/diego_ma},
  booktitle = {Proc. AAAI-98},
  interhash = {42e8183f7f62885a9d4c369a42c8ed4d},
  intrahash = {79604d45adda39d78379de3eb1af3f40},
  keywords = {web_data_extraction},
  timestamp = {2007-12-14T02:37:51.000+0100},
  title = {Learning to Extract Symbolic Knowledge from the World Wide Web},
  url = {http://citeseer.nj.nec.com/9546.html},
  year = 1998
}

Endnote record

%0 Conference Paper
%1 Craven:1998
%A Craven, Mark
%A DiPasquo, Dan
%A Freitag, Dayne
%A McCallum, Andrew
%A Mitchell, Tom
%A Nigam, Kamal
%A Slattery, Se\'{a}n
%B Proc. AAAI-98
%D 1998
%K 
%T Learning to Extract Symbolic Knowledge from the World Wide Web
%U http://citeseer.nj.nec.com/9546.html
%X The World Wide Web is a vast source of information accessible to computers, but understandable only to humans. The goal of the research described here is to automatically create a computer understandable knowledge base whose content mirrors that of the World Wide Web. Such a knowledge base would enable much more effective retrieval of Web information, and promote new uses of the Web to support knowledge-based inference and problem solving. Our approach is to develop a trainable information extraction system that takes two inputs: and ontology defining the classes and relations of interest, and a set of training data consisting of labeled regions of hypertext representing instances of these classes and relations...

BibSonomy :: publication ::

Learning to Extract Symbolic Knowledge from the World Wide Web

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Abstract

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Endnote record