Decision Guidance for Optimizing Web Data Quality - A Recommendation Model for Completing Information Extraction Results

Abstract

Incomplete information in web intelligence applications has serious consequences: inaccurate statements predominate, resulting primarily in erroneous annotations and ultimately in inaccurate reasoning on the web. This research work focuses on improving the completeness of extraction results by applying judiciously selected assessment methods to information extraction within the principle of complementarity. On the one hand, this paper discusses several requirements an assessment method must meet in terms of process ability and profitability to guarantee effective operation in a complementarity approach. On the other hand, it proposes a recommendation model to guide an IE system designer in selecting the appropriate methods for optimizing web data quality. The paper concludes with an application scenario that supports the theoretical approach.