Automatic Web-based relational data imputation

Abstract

Data incompleteness is one of the most important data quality problems in enterprise information systems. Most existing data imputing techniques just deduce approximate values for the incomplete attributes by means of some specific data quality rules or some mathematical methods. Unfortunately, approximationmay be far away from the truth. Furthermore, when observed data is inadequate, they will not work well. The World Wide Web (WWW) has become the most important and the most widely used information source. Several current works have proven that using Web data can augment the quality of databases. In this paper, we propose a Web-based relational data imputing framework, which tries to automatically retrieve real values from the WWW for the incomplete attributes. In the paper, we try to take full advantage of relations among different kinds of objects based on the idea that the same kind of things must have the same kind of relations with their relatives in a specific world. Our proposed techniques consist of two automatic query formulation algorithms and one graph-based candidates extraction model. Several evaluations are proposed on two high-quality real datasets and one poor-quality real dataset to prove the effectiveness of our approaches.

Keywords

data incompleteness / imputation / World Wide Web / query formulation / candidate selection / semantic relation

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