Self-supervised relation extraction from the web

Ronen Feldman*, Benjamin Rosenfled, Stephen Soderland, Oren Etzioni

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations


Web extraction systems attempt to use the immense amount of unlabeled text in the Web in order to create large lists of entities and relations. Unlike traditional IE methods, the Web extraction systems do not label every mention of the target entity or relation, instead focusing on extracting as many different instances as possible while keeping the precision of the resulting list reasonably high. SRES is a self-supervised Web relation extraction system that learns powerful extraction patterns from unlabeled text, using short descriptions of the target elations and their attributes. SRES automatically generates the training data needed for its pattern-learning component. We also compare the performance of SRES to the performance of the state-of-the-art KnowItAll system, and to the performance of its pattern learning component, which uses a simpler and less powerful pattern language than SRES.

Original languageAmerican English
Title of host publicationFoundations of Intelligent Systems - 16th International Symposium, ISMIS 2006, Proceedings
PublisherSpringer Verlag
Number of pages10
ISBN (Print)354045764X, 9783540457640
StatePublished - 2006
Externally publishedYes
Event16th International Symposium on Methodologies for Intelligent Systems, ISMIS 2006 - Bari, Italy
Duration: 27 Sep 200629 Sep 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4203 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Symposium on Methodologies for Intelligent Systems, ISMIS 2006


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