Open information extraction from the Web

Details Open information extraction from the Web Open information extraction from the Web

2011-01-25

2011-01-25

Holmes, Michael B. (Department: 2129)

Data processing: artificial intelligence

Neural network

Learning task

C705S005000, C382S104000

Reexamination Certificate

active

07877343

ABSTRACT:
To implement open information extraction, a new extraction paradigm has been developed in which a system makes a single data-driven pass over a corpus of text, extracting a large set of relational tuples without requiring any human input. Using training data, a Self-Supervised Learner employs a parser and heuristics to determine criteria that will be used by an extraction classifier (or other ranking model) for evaluating the trustworthiness of candidate tuples that have been extracted from the corpus of text, by applying heuristics to the corpus of text. The classifier retains tuples with a sufficiently high probability of being trustworthy. A redundancy-based assessor assigns a probability to each retained tuple to indicate a likelihood that the retained tuple is an actual instance of a relationship between a plurality of objects comprising the retained tuple. The retained tuples comprise an extraction graph that can be queried for information.

REFERENCES:
patent: 7499892 (2009-03-01), Aoyama et al.
patent: 2003/0061201 (2003-03-01), Grefenstette et al.
patent: 2005/0262050 (2005-11-01), Fagin et al.
patent: 2005/0278362 (2005-12-01), Maren et al.
patent: 2006/0184473 (2006-08-01), Eder
An Automated Multi-component Approach to Extracting Entity Relationships from Database Requirement Specification Documents,Siqing Du and Douglas P.Metzler, ISSN 0302-9743, pp. 1-11, Jul. 4, 2006.
Lin, Dekang and Patrick Pantel. “DIRT—Discovery of Inference Rules from Text” University of Alberta. Department of Computing Science. Edmonton, Alberta T6H 2E1 Canada.

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