Utility-based resolution of data inconsistencies
Proceedings of the 2004 international workshop on Information quality in information systems
Is it the right answer?: exploiting web redundancy for Answer Validation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Data & Knowledge Engineering
DEXA '11 Proceedings of the 2011 22nd International Workshop on Database and Expert Systems Applications
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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.