Populating the Semantic Web by Macro-reading Internet Text

A key question regarding the future of the semantic web is “how will we acquire structured information to populate the semantic web on a vast scale?” One approach is to enter this information manually. A second approach is to take advantage of pre-existing databases, and to develop common ontologies, publishing standards, and reward systems to make this data widely accessible. We consider here a third approach: developing software that automatically extracts structured information from unstructured text present on the web. We also describe preliminary results demonstrating that machine learning algorithms can learn to extract tens of thousands of facts to populate a diverse ontology, with imperfect but reasonably good accuracy. 1 The Problem The future impact of the semantic web will depend critically on the breadth and depth of its content. One can imagine several approaches to constructing this content, including manual content entry by motivated teams of people, convincing owner...

• Type: Conference
• Year: 2009
• Where: SEMWEB
• Authors: Tom M. Mitchell, Justin Betteridge, Andrew Carlson, Estevam R. Hruschka Jr., Richard C. Wang
• Keywords: Internet Technology, Semantic Web, SEMWEB 2009, Structured, Unstructured Text Present