Tackling incompleteness in information extraction - A complementarity approach

Abstract

Incomplete templates (attribute-value-pairs) and loss of structural and/or semantic information in information extraction tasks lead to problems in downstream information processing steps. Methods such as emerging data mining techniques that help to overcome this incompleteness by obtaining new, additional information are consequently needed. This research work integrates data mining and information extraction methods into a single complementary approach in order to benefit from their respective advantages and reduce incompleteness in information extraction. In this context, complementarity is the combination of pieces of information from different sources, resulting in (i) reassessment of contextual information and suggestion generation and (ii) better assessment of plausibility to enable more precise value selection, class assignment, and matching. For these purposes, a recommendation model that determines which methods can attack a specific problem is proposed. In conclusion, the improvements in information extraction domain analysis will be evaluated. © 2012 Springer-Verlag.