Learning to Extract Symbolic Knowledge from the World Wide Web

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 world wide knowledge base whose content mirrors that of the World Wide Web. Such a knowledge base would enable much more e ective retrieval of Web information, and promote new uses of the Web to support knowledgebased inference and problem solving. Our approach is to develop a trainable information extraction system that takes two inputs: an ontology de ning 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. Given these inputs, the system learns to extract information from other pages and hyperlinks on the Web. This paper describes our general approach, several machine learning algorithms for this task, and promising initial results with a prototype system.

• Type: Conference
• Year: 1998
• Where: AAAI
• Authors: Mark Craven, Dan DiPasquo, Dayne Freitag, Andrew McCallum, Tom M. Mitchell, Kamal Nigam, Seán Slattery
• Keywords: AAAI 1998, Intelligent Agents, Knowledge Base, Wide Knowledge Base, World Wide Web