Characterization and Resolution of Incompleteness in (World-Wide-Web) Information Extraction

Low information quality is one of the reasons why information extraction initiatives fail. Incomplete information has a pervasive negative impact on downstream processing steps. This work addresses this problem with a novel information extraction approach, which 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, various types of incompleteness are identified and an approach to their automatic detection is presented. Further, a prototype generic framework that incorporates the complementarity approach is proposed.