Populating the Semantic Web by Macro-reading Internet Text

Lecture notes in computer sciencePublished 1 January 2009Open access

Tom M. Mitchell, Justin Betteridge, J. Andrew Carlson, Estevam Hruschka, Richard Wang

Citations50

TL;DR

A third approach is considered: developing software that automatically extracts structured information from unstructured text present on the web, and preliminary results demonstrating that machine learning algorithms can learn to extract tens of thousands of facts to populate a diverse ontology are described.

Abstract

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.

Keywords

Computer Science

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