Abstract: Data quality assessment of Web article content helps identify useful data.Exiting approaches not only heavily rely on lexicon features or user interactions to obtain quality indicators,but also can not capture the content’ semantics.A fact-based quality assessment (FQA) approach was proposed in this article.Given one target article,the approach starts with the identification of alternative context by collecting relevant articles and extracting facts from every article.Then,the accuracy baseline is constructed by voting,and the completeness baseline is constructed by iterations over fact graphs.Finally,data quality dimensions,including accuracy and completeness are calculated by comparing the facts of the target article with the established dimension baselines.Based on the facts of target article content,rather than particular features,FQA approach can quantify data quality dimensions with high precisions.The superior performance of FQA was verified in the experiments.

Key words: Data quality,Web article,Accuracy,Completeness,Quality dimensions,Fact