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Using Wikipedia to bootstrap open information extraction

Using Wikipedia to bootstrap open information extraction Using Wikipedia to Bootstrap Open Information Extraction Daniel S. Weld Computer Science & Engineering University of Washington Seattle, WA-98195, USA Raphael Hoffmann Computer Science & Engineering University of Washington Seattle, WA-98195, USA Fei Wu Computer Science & Engineering University of Washington Seattle, WA-98195, USA weld@cs.washington.edu raphaelh@cs.washington.edu wufei@cs.washington.edu in the corpus and R denotes the number of relations; in contrast, scalability to the Web demands that open IE scale linearly in D. 1. INTRODUCTION We often use ˜Data Management ™ to refer to the manipulation of relational or semi-structured information, but much of the world ™s data is unstructured, for example the vast amount of natural-language text on the Web. The ability to manage the information underlying this unstructured text is therefore increasingly important. While information retrieval techniques, as embodied in today ™s sophisticated search engines, offer important capabilities, they lack the most important faculties found in relational databases: 1) queries comprising aggregation, sorting and joins, and 2) structured visualization such as faceted browsing [29]. Information extraction (IE), the process of generating structured data from unstructured text, has the potential to convert much of the Web to relational form ” enabling these powerful querying and visualization methods. Implemented systems http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGMOD Record Association for Computing Machinery

Using Wikipedia to bootstrap open information extraction

ACM SIGMOD Record , Volume 37 (4) – Mar 20, 2009

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References (30)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2009 by ACM Inc.
ISSN
0163-5808
DOI
10.1145/1519103.1519113
Publisher site
See Article on Publisher Site

Abstract

Using Wikipedia to Bootstrap Open Information Extraction Daniel S. Weld Computer Science & Engineering University of Washington Seattle, WA-98195, USA Raphael Hoffmann Computer Science & Engineering University of Washington Seattle, WA-98195, USA Fei Wu Computer Science & Engineering University of Washington Seattle, WA-98195, USA weld@cs.washington.edu raphaelh@cs.washington.edu wufei@cs.washington.edu in the corpus and R denotes the number of relations; in contrast, scalability to the Web demands that open IE scale linearly in D. 1. INTRODUCTION We often use ˜Data Management ™ to refer to the manipulation of relational or semi-structured information, but much of the world ™s data is unstructured, for example the vast amount of natural-language text on the Web. The ability to manage the information underlying this unstructured text is therefore increasingly important. While information retrieval techniques, as embodied in today ™s sophisticated search engines, offer important capabilities, they lack the most important faculties found in relational databases: 1) queries comprising aggregation, sorting and joins, and 2) structured visualization such as faceted browsing [29]. Information extraction (IE), the process of generating structured data from unstructured text, has the potential to convert much of the Web to relational form ” enabling these powerful querying and visualization methods. Implemented systems

Journal

ACM SIGMOD RecordAssociation for Computing Machinery

Published: Mar 20, 2009

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